{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# This file gives a brief overview of the capabilities of the code."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* If you want to predict the spectrum of a star with particular labels, you'll want the \"spectral_model\" package.\n",
    "* If you want to fit an observed spectrum, see the \"fitting\" package.\n",
    "* Downloading and processing APOGEE spectra is handled by the \"process_spectra\" package.\n",
    "* The \"utils\" package contains some general-purpose functions used by the other packages.\n",
    "* If you want to get under the hood and train your own models, you should read into and run training.py."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.Fitting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model interpolator requires you to pass it the trained neural network (really, a list of biases and weights parameterizing the network), so we read in the network we'll be using at the beginning and then pass it to various functions as we go. This is a bit cumbersome, but the advantage is that if you train a new network (with architechture compatible with the existing code) you can just pass it to the relevant functions without having to rewrite everything."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import absolute_import, division, print_function # Python2 compatibility\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline \n",
    "\n",
    "from The_Payne import utils\n",
    "from The_Payne import spectral_model\n",
    "from The_Payne import fitting\n",
    "\n",
    "# the following will be used throughout all routines\n",
    "# these are the default for fitting APOGEE spectra \n",
    "# substitutes them if you train different neural networks for other purposes\n",
    "# the instruction for training a new Payne is included at the end of this tutorial\n",
    "\n",
    "# read in the default wavelength array, \n",
    "#         the apogee mask/filter used for fitting spectra : True = excluded\n",
    "wavelength = utils.load_wavelength_array()\n",
    "mask = utils.load_apogee_mask()\n",
    "#mask = np.zeros(wavelength.size) # no masking\n",
    "\n",
    "# read in the default neural net\n",
    "NN_coeffs = utils.read_in_neural_network()\n",
    "w_array_0, w_array_1, w_array_2, b_array_0, b_array_1, b_array_2, x_min, x_max = NN_coeffs\n",
    "\n",
    "# if you trained your own neural net (see last part of this tutorial),\n",
    "# you can load in your own neural net\n",
    "#tmp = np.load(\"NN_normalized_spectra.npz\")\n",
    "#w_array_0 = tmp[\"w_array_0\"]\n",
    "#w_array_1 = tmp[\"w_array_1\"]\n",
    "#w_array_2 = tmp[\"w_array_2\"]\n",
    "#b_array_0 = tmp[\"b_array_0\"]\n",
    "#b_array_1 = tmp[\"b_array_1\"]\n",
    "#b_array_2 = tmp[\"b_array_2\"]\n",
    "#x_min = tmp[\"x_min\"]\n",
    "#x_max = tmp[\"x_max\"]\n",
    "#tmp.close()\n",
    "#NN_coeffs = (w_array_0, w_array_1, w_array_2, b_array_0, b_array_1, b_array_2, x_min, x_max)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's use the data-driven spectral model to emulate the APOGEE-like spectrum of a single star similar to the Sun."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(26,)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.7, 1.05)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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F92/uYjKZ5KZNm+SKFSvk4cOH270vTCaTfOaZZ+TevXsVHau2tlZOnz7d4nvLXgyzZ8+WJ06ccGg/Og9AtlSQyzj6o0aCdD2ArwEIAL8C8LN5eTyAkwDizD8nAcTbO5ejCZKUUmZlZcnS0lKb2+zYscOpL54VK1bIH3/80eK6kpISOW3aNLl9+3abxzAajfJvf/ubXLp0qWxoaJBGo1Fu2rRJ/uUvf5HPPvusXL9+vXz33XfltGnT5I4dOxyO0ZecPHlSzp071+EvkLKyMjl16lSZm5vr0H7V1dVy9uzZ8ptvvpHTpk1zaN+25s2bJysqKhzer6mpSZ4+fVpu27ZNfvDBB3LKlCle8yV47NgxOXXqVLl//36H983Ly5Nz586VTz31lJw3b578v//7P9nc3OyGKF1jMplkUVGRw+83rRgMBjl16lSvS6R9xZkzZ+SUKVPkuXPntA5FzpkzR9FnYtasWbK8vFy18x49elQ+99xzFtc1NzfLRx99tNMN8O7du+Uzzzxj87gfffSRnD9/vty0aZNctWqVnD9/vmaNekajUT722GN2b+RNJpPMzs6Wn3/+uTxx4oQ0Go3SZDLJDRs2yEceeaTd/i3vHWcb4tQyZ84czb6vHEnsWzzxxBNO/5vNnz9f1fe+OxgMBjl37lxF2y5evFjm5eU5dZ7GxkY5Y8YMp/b1tPz8fLlo0SK5YMECOW/ePPn888/LwsJCzeLRLEECsBrAWQAGAPkA/gDgYQAPm9cLAK8AOAFgP4CRbfb9PYDj5p/fKQnImQSpuLjY6gVByvNZ/aOPPurUl47JZJIzZ86UJSUl7Zbn5+fLKVOmtGtFt+fYsWPy6aeflgsXLpRffvllpxY7k8kkly5dKrds2eJwnL6gqalJTpkyxemenMbGRjl37ly5bds2xfssX75cnjx5Ukop5VtvvSV/+eUXh8974sQJ+Y9//MPh/Sxp+RI8deqUKsdzRtteT3/ptfQleXl5ct68eVqH4XP0er2cOnWqSz3FatqyZYtcu3atzW0OHz4sV65cqfq533rrLbl58+ZOy59//nl58OBBi/ts2LBBLl++vNNyk8kkX3jhhU6/S25urnzsscc0uZl/8cUX5Z49e1w6Rm1trZw7d65cuHChfPHFF+X8+fMtjgLxtI8//thuo6u7PPHEEw5fE4qKiuTixYsdPldDQ4NctGiRw/tpISsry26jS2VlpXz22WddOs/777/vNaMwrFm5cqV87rnn2t37VlRUyCeffFJ+9dVXmsSkaQ+SJ3+cSZCklHLBggVWWzEWLFjgdFYvpZRVVVVy2rRprQnN3r175WOPPeaWVl6TyaTZRcfd/vvf/8pvv/3WpWOYTCb5l7/8RVGLVUNDg5wzZ067108++aTD55wzZ46sra11eD9rKioqXP4idVZZWZl89NFHrfaKknd47bXX5PHjx7UOw6c89dRTiocGeYLJZJKzZs2yuc28efPcdh2ZOXNmu5u6//73v/Kdd96xud+GDRtkVlZW6/WnqqpKPvHEE3Lr1q0Wt//+++/lf/7zH/UCV+CDDz6Q7733nmrHa25ulsXFxaodz1X19fVywYIFHj+v0Wh0umFm3rx5Dt+zfP/993L9+vVOnc/TTp06JV9++WWb26xcuVIePXrUpfOYTCanG/PdzWQyyRdffNHm/9m///1v+dFHH3kwqvPclSB1mbrEDz30EF599dVOyzdu3IiBAweie/fuTh87OjoaDz74IObNm4e//OUvWL9+PZYuXeqWB/CFEJgwYQI2bdqk+rG1tmXLFlx22WUuHUMIgccffxyvvfaa3W0//fRT3Hnnna2vWyrwlJWVKT7fyZMnkZ6ermrZ99jYWOj1+nbz/XiCXq/HU089hcWLFzv00Cl53j333IN3331X6zB8xqZNmzBkyBCvKnAhhEDPnj1hrSprSUkJoqKi3HYdmTdvHp555hl88803WL58OY4cOYJ7773X5n5XX301rrnmGsyfPx9PPPEEXnjhBUydOtViURYAGD9+fGvFRk9YtWoVGhoaMGnSJNWOqdPpkJSUpNrxXNVSjtrT14f9+/fjoosucmrfnj174syZMw7ts3XrVqvvK2/T8vudvxe37Pjx4+jXr59L5xFC4I477uj03V9TU4OXXnoJDz/8MI4ePerSOZy1dOlSjB49Gr/5zW+sbvPAAw8gLy8P27Zt82Bk7tNlEqS0tDSYTCYUFxe3LisvL8fXX3+Nu+66y+XjX3DBBViyZAlefPFFzJ49261VZq6//nqsW7fObcfXQnNzMwCoUokuNTUVzc3NKCkpsbndgQMHMHTo0HbLJk+ejPfee0/xudasWYN77rnHqThtmTBhAr799lub2zQ1NSE3N1eV80kpMW/ePCxatAgxMTGqHJPcJyoqCgaDAfX19VqH4hO++uor3HHHHVqH0cntt9+OTz75xOK6d955Bw888IDbzh0XF4e///3vaG5uxoQJEzBt2jRF+40YMQJZWVlYsmQJsrKykJ6ebnP7GTNmYOXKlWqEbNWmTZswa9YspKene9W0D+5y5ZVXYvPmzR4956ZNm3DllVc6te/YsWMdviluqV7rK4YNG4YDBw5YXHfs2DGXk6MW48aNw5EjR1obchsbG/HEE0/g5ptvxvLly93+WbPkX//6FwYNGqSogXv69On46quvcOzYMQ9E5l5dJkECgKlTp7ZeEEpLSzF//nw89dRTqs5D5Ik5jYQQSEtLa1dy2ddt2bIFV1xxhWrHmzJlClatWmV3u47/X2lpaTh79qyic5hMJlRVVaky10NHV111lc1ewl27dmHWrFl48cUXUV1d7fL5Nm7ciGuvvRbdunVz+VjkGXfddRfef/99rcPwetnZ2Rg5cqRXzjeXnJxstSGnrKwMycnJbj2/EAI33HADLrig0wwdqklMTERVVZVbjm0wGLB48WKUl5fjr3/9K6655hq3nMfbXH755R5vhS8rK3P6+jBo0CAcPnxY8fYNDQ0ICQlx6lxaueaaa7B+/XqL6z788EPcfvvtqp1r5syZmD9/Pt555x3MmjULs2fPRo8ePRASEoLRo0fj+++/t3sMW71djtixYwcMBgOuv/56RdsLIbBo0SK8/PLLDo3W8UZdKkGKjY3FbbfdhoULF2LJkiV44YUXLE4W6QtGjx6Nn3/+WeswVPPdd9+pmiDFx8dDr9dbXV9aWmp1ItLU1FQUFBTYPcemTZtw1VVXOR2jLQEBAYiMjLT4OxiNRrz77rtYtmwZFi5ciOXLl7t8vvXr1/vNzUVXMWTIEBw/ftzl4+zYsUNxo4Av+uijj3DbbbdpHYZVaWlpnb5vcnJyutSErgMHDnToBlmpZ599FpMmTcJtt93mlQmwuwQHB8NgMHjsfAaDwaXRHQEBATCZTIq33759O8aMGeP0+bQQHx9vdShpTU2NqiMz4uLikJWVhTFjxmDRokXtJp2+7bbbsHbtWpv7v/HGG3jyySddjqOhoQH/+c9/8NBDDzm0X2BgILKysrBw4ULk5OS4HIdWulSCBACjRo3Cs88+i7///e+IiorSOhynDR8+HLt27dI6DNUIIVSf6DU8PBy1tbUW1/3444+49NJLLa675ZZb8Nlnn9k9/saNGzFhwgSXYrRl4sSJFlukvvrqK9xyyy0ICAhASkoK4uPjceTIEafP88svv+DCCy/0qxuMrsLV/zO9Xo/3338f77zzjqLn9rSUl5eH1157zaGW84KCAiQlJbl98mFX3HzzzZ2+bz777DPcfPPNGkWkvptuusnuTZujioqKEBUVpdrQJV+j0+lah6a7286dO12eLNjW9bijbdu2+czzR23Fx8fj3Llz7ZYdOXIEAwYMUP1cCQkJ6Nu3b6cRLEII9OjRw2qj1/vvv4/IyEiEhYWhpqbGpRiWLVuGGTNmOHUdioqKwrJly7BmzRq7jxN4qy6XIHUV4eHhXeb5A1dbp6wZN24cfvrpJ4vrfvnll07PH7XIyMhAfn6+zWNXVFQgOjraqRntlRoxYoTFJPiHH35ol9zde++9Lt18fPjhh6o+1EyeY+mC7IjXX38djz/+OGbPno2ioiKv/U754Ycf8MYbb+D666/Hvn37FCdz7npGUE0ZGRnIy8trfS2lREVFBeLi4jSMSl2xsbGorKxU9ZhvvfUW7r//flWP6UsuuOACq8+8qG3Xrl0uJ0ijRo1Cdna2om0bGxtVLXzkKZaG2X3xxRf4f//v/3k0jhtvvNHiPUFDQwP27duHSZMm4e6773boeeuOKisrYTAYkJmZ6fQxAgMD8fTTT+PQoUP4/PPPnT6OVpggeTm1xpFq6cSJE25pBRw9ejS2b99ucZ3RaLSZlKWlpVmtLgWcv/FSo7iHLUIIBAUFoampqXVZS2tU2xabmJgYl59DCg4Odml/0sbQoUPxyy+/OLVveXk59Ho9MjIyAAA33HCDVxZ/qa+vx8cff9w6lOThhx9Genq63QuqlBKlpaVuf45HDYMHD27tGVu/fn2XrCLZu3dvnDp1SpVjNTQ0oLq62q+fmRwzZozV65vaysrKrA5JV2r06NHYsWOHShF5J0tJa0VFBRISEjwaR69evSzev6xduxa33norAKBv3744ceKE0/eQb775Jn73u9+5FGeLadOm4fDhw6oMGfckJkherE+fPjhx4oTWYbjs4MGDGDx4sOrHDQ0NRWNjY6fler0eERERNve9//77bRZ5OHXqFHr37u1yjPZcccUV2LJlS+vrt956C3fffXen7ZwdbqHGhY+0M3ToUOzdu9epfT/55BNMnjy59fXIkSOxc+dOtUJTzT/+8Q88/vjj7RoFbrzxRvz00082y0fv3r0bI0aM8ESILrvvvvuwZs0aFBcX49tvv/V4i7MnDBs2DPv27VPlWOvWretSQxCdkZ6eruhZWbW4Opw3Ojpa0RA7X270FUIgISGhtaT5mTNnXJpCxhXR0dGdiqPs2rULw4cPb309fPhw7N+/3+FjGwwGFBUVqfq7PfbYY1ixYoVqx/MEJkherKu0yLjzgWRL4563b9+OX/3qVzb3i46ORmhoqMUKU/v27XNr1ae2xo8fj6+++gpSShw4cAC9evWyOPRg2LBh2LNnj8PHV/JvQd4rLi7O6WqWp0+fbjc8QgiBmJgY1YdCuSInJwcRERHo2bNnp3Vz5szB3/72N6v7rl271mcSDSEEpk2bhttvvx1PPPGE1uG4xaBBg3Do0CFVjrVv3z4MGzZMlWORdyktLfWqeacc9dBDD2HFihUwmUxYunSpxQZNT7juuuvajQgoKChAWlpau0T3oosucqp4ijsK34SGhmLo0KEe6xVVAxMkLzZgwACfrgDSoqGhwS2TIQLn51/YunVru2U7duxQNJ76T3/6E9544412y6SU+Ne//oXf/va3qsZpTWBgIO644w4sWrQI//73v/H73//e4nbjx4/HDz/84PDx9+zZwxsNH+dKy27HfW+++WZ88cUXroakmtdffx1//vOfLa6Li4tD7969LbaA1tXVQUqJ0NBQd4eomn79+mHjxo1umTbAG0RERKCurk6VY9kbIu0vEhISXHoGUYnm5ma3Pmvb0YkTJ9CnTx+PnU9tkZGRyMjIwPTp0/G73/1Os0rJF154IbZv3946ofDbb7/d6bGA3r17OzwKSUqJXbt2YeTIkarF2mLSpEn4+OOPVT+uuzBB8mIBAQFdovKYO3+HMWPGdCrUUF9fr+gB0MTERPTq1QvLly9vLVH6wgsv4P777/foA6Rjx47FjTfeiIkTJ1qtxpWQkODUbPWNjY0+N98EtRcSEuJwcYXy8nKLF+6BAwd6zQR+n332Ga6++mqbz8fdf//9ePPNNzstf/vtt3Hvvfe6MTr34LOA9hkMBo/esHszV55BVCovL69dGWlXSCntDqE7ceKER4avu9Pvf/973HzzzVYLQXmCEAKTJ0/Gm2++iZ9//hnR0dGdnse09hiCLT/++CPGjx+vZqitAgICkJGR4dGho65ggkRu1dzcjIAA973NgoOD2xU5aGpqcqjk7z333IPx48fjySefxIIFCzB06NB2Y3g9Zfjw4fj1r39tcxshhENzTRiNRt5odAEXXnghDh486NA+1uYZEUJ4xTNJsN4AACAASURBVDMAb775JgoLC3HdddfZ3C4oKAiXXHJJu97T5uZm5Obmdql5hLqKgICA1hZtZx04cAAXXnihShH5tosuusjtCdLx48fRt29fVY4VFxdnd9LgU6dOoVevXqqcTyvBwcFunQJEqREjRuDEiRNYvXo1pkyZosoxv/jiC9xwww2qHMuSO++802cmQGcfNrlVbm6u27vTk5OTUVRUhJSUFOzevdvhBGf48OGaJEWO6tmzJ/Ly8iw+r2HJgQMHPPYsFblPSwU0R4Y87N69G7NmzbK4LiQkBA0NDZoNT1u1ahWSk5MVz8x+++23Y+bMmRgwYACSkpLwySefeGwILDmmpbCQK8nrjh073HqD5kvi4+OdfgZRqePHj6tWEKNHjx44c+aMzWFnBoOBPakqmjNnDoQQqozU2b9/P/r06ePWhtXk5GQUFxdDSun1I6TYg+TloqOjXS7xrKVDhw65pYJdW1dffTU2btwIANi6dWuXLKELnH+GwZHhUbt27cKoUaPcGBF5QmpqKoqKihzax1YC5IlhO9YcOXIEpaWlipMj4HyvxOLFi7Fo0SI8++yzyM/Px+jRo90YJTlryJAhDvd2dlRQUKBZZTB/1NK4qIaWBIk8JyYmBtHR0VbXx8bGKkqy6+vrsXLlSvzxj39UMzyLRowYgd27d7v9PK5iguTlunfv3m6SQV+Tk5Pjllmm2xo0aBAOHz4MKSVKSkq67EPQjiZIeXl5rXPgkO9y9OF3o9Foc1jrqFGj8PPPP6sRmsNeffVVzJw50+H9oqKisGDBAjz00EOYMWOGGyIjNQwcOBBHjhzROowuJTAwEAaDwa3nUKslv0ePHj59v9IVKb1vWLJkCebMmeORYfnWJrr1NkyQvFzHWdh9TV1dnd05iVzV8mzOnDlzcOONN7r1XFpKTU3F2bNnFW9vMpn4DJIfKiwstJkYJycnWyxv724VFRXIzMx0ujpZcnKyX08c6gvCw8MdLijSVl1dnUcL5PiCAQMG4OjRo52WGwwGh55J9YRu3bqhuLjY6vq6ujq3VbQly/r162fx/dNWfn4+EhMTPdZzGxoaCqPR6PbE31VMkLxcRkYG8vPztQ7D6y1ZsgQvvvhilx1eB7i3GiB1HZ54CPqtt95yeB93TRhNXceZM2d8/gF+tV188cUWh8RmZWXhqaeewvLly50+tslkUvW6EhAQYLMITG5urs9XsPM1mZmZOHnypM1tVq9ejUmTJnkoovOuvfZafP311x49p6OYIHk5R3sNiMi/nT592m4hj5iYGLvVpqzZsWMHPvvsM2RnZzu038GDBzFkyBCnzkm+w5Ub7tOnT6tWcrqr6Nu3r8UhUgaDAVlZWS4VcTh79ixSU1NdCc8hTJA8Lzg42GZPjZQSZWVlHu+dHzt2LLZt2+bRczqKCZKXCwwMRHNzs9ZhkJfQ6XSK3g96vd7tQxvJcxwpzX3mzBm7N5kDBgxwej6kjz/+GGvWrMF7773n0H4FBQVIT0936pzkO1y5Zp05c0ZxlU5/ERgY2Kl0en5+futnKTAwsN1UF44oLCz0aEEMNedcIuVsNVr8+OOPuPTSSz0YzXlCCMTFxTk1v6OnMEEi8iE9e/ZUVCXo5MmTyMzM9EBE5AnBwcGKJ/xrbGy0W8I7MzMTp06dcjiOkydPIiMjAyEhIRg7dmy7+YmU4DDRri8hIQHnzp1zat/CwkKP9mj4krZJ0oYNG/Cb3/wGwPlCTs4Owy8sLERaWpoq8bWw1YhXWlqKxMREVc9H9tlqYNuwYQMmTpzowWj+5/bbb8cHH3ygybmVYIJEDnnrrbcUTwSo1+sRGRnp5oj8i9KKNBzK0LWkpKTYfPjZUT179rQ7Lt2S1atX4/777wcA3HLLLdi0aZNqMVHXkJSU5HQREBaWsWz06NHYvn176+vjx4+3zi/Ys2dPnD592qnjuiNBSktLs/pYgJTSrRPHk2X2hlQHBQV5MJr/6d27t1PXIU/hO9UHCCEcGmLjTu+99167L2pb1Jxfgc5TmiCxB6lrSUlJcXguJFuioqKg1+sd3q+xsbF1zg2dTqe4ita5c+e6bPl9aq9bt24oLS3VOowu5aqrrmptjDCZTAgICGjtjXUlQSopKVH92ZOMjAzOheRlrL1H1C7S4Yz+/fvbrbKnFUUJkhBiohAiRwhxXAgx18L6nkKIb4UQ+4QQ3wkhurdZZxRC7DX/eH/hcy8UGxuLyspKrcOA0WjExRdfrLjV+OzZs0yQVNatWzdFrbPl5eWIi4vzQETkCUoTJE83pCg9Hws0+A8mSOoLCwtrLZ/+7bffYsyYMa3rXBli15JsqSk9PR2FhYWqHpNcYy1BOnXqlOYNqbfddhs+/PBDTWOwxu4nQwihA/AKgGsBDAYwSQjRsVbr3wC8LaW8CMBiAEvarKuXUg41/3TdSWrcyJUvQDXl5ORg2LBhiue5KCoq4nhylTnS2qN1yxCpR2mCVFJSgqSkJLfFYek9pSRJYoLkP5wdYmdvgmN/16NHDxw6dAjr1q3Dtdde27rcXpUyT0tLS2OC5GV69epl8ZlTb/hejomJQW1traYxWKPk2+gSAMellLlSyiYAawDc1GGbwQC+Nf99s4X15AJvmSx29+7dGD58uOKEjUPs3IOJj/9JSkpS9AySkhLfLRwdutvU1NRpklelN0OeLidM2omPj3eqMlVxcTGvFzZcf/31uO+++zB79myvvgZYq0zGSWK1k5iYiLKysk7LvWVuuqSkJKcLu7iTkgQpHUDbu/N887K2fgHwW/PfbwEQJYRIML8OFUJkCyG2CyFutnQCIcSD5m2y2TXfWXp6OgoKCrQOA8eOHUO/fv1w3XXXYd26dXa353MH2pBSevUFlBwXFBSkqHSyIwmSo0Oh8vPzO5UE7t+/v6Jn4oQQfE/6iYCAAMXPprWlpDy9P8vIyMDq1au9vqHB2ufcHQUhSBlrjWF1dXVeMR3IiBEjsHv3bq3D6ERJgmTp3d7xX3oWgMuFEHsAXA6gAEDL1byHlHIkgLsBLBNC9Ol0MClXSilHSilHenqyKl9gLfv3tJbxyr169VLUo8WKNe4RHR2N6upqq+vdPcyKvJcjCZK1YReOHFtp0RAmR2QP50Cyr3///haXCyEcTkobGxsRHBysRliKFBYWch40smjo0KHYs2eP1mF0ouTuNR9ARpvX3QG0G1MhpSyUUt4qpRwGYJ55WVXLOvOfuQC+AzDM9bD9S2hoqOI5UNyl48OcTHy0Y2+IY15enkcn/yPvodfrW6vM2aNGguQtvdvk+06fPo2MjAz7G1InzlS5PHv2rEd7dNiD5F2am5u9pqS+s1VV3U3JXe5OAP2EEJlCiGAAdwFoV41OCJEohGg51hMAVpmXxwkhQlq2ATAOwCG1gifPaRleR9rr3r27zR68goICttT5KUeeKerZs6dDCZKlIXYBAQFeMwUB+Ta9Xo+oqCitw/BJzpT6dmfCYqnHmNclbYWHh6Ourq719fHjx3lPZ4fdBElK2QzgEQDrARwG8IGU8qAQYrEQoqUq3RUAcoQQRwEkA8gyLx8EIFsI8QvOF294XkrJBMnNGhoasHPnTlWPefDgQVxwwQWqHpOck5GRYbMHiReirkvNZCQyMrLdBdMeg8Hg1JCchoYGhISEOLwfESnjbQlSREREpx6B6upqJsAa6tGjR7v3yIEDBzSvYNdWbGwsKioqtA6jHUXjpKSU66SU/aWUfaSUWeZlC6SUa81//0hK2c+8zR+llI3m5duklBdKKS82//kv9/0qXZsjN0YffPABVq9erer5CwsL27Ue2xvzzJKt7pOammqzchifQeqaoqKiUFNTY3MbR5/1USPhsvddUFpaqvpklOTddDqdoqIibfE5Ned5W4KUlpaGs2fPdlrO/2PtdHyPeEsFuxbDhw/3ukINvIPtgvbv3696S01ZWRkSEhJaX8fHx9vM9ktLS3mT7ib2KpqZTCavGVtM6klISLBZClVKqclwN3vPxDFB8j+JiYleWba3q4qIiFA8P2ELd1aZTUtL47OJXqZXr17tEqTm5mYEBQVpGFF7w4YN87pCDUyQupi9e/fi4osvVv24HUtH2ysRXFRUhOTkZNXjIPJX9hol6urqEBkZ6dAxlbbodizS0lb37t1t3gwxQfI/3bp1c2qyWPIsd/XocLJY79N25InJZPK63ryYmBi7IyQ8jQmSjwgLC1P0vMDHH3+M2267DZGRkW59s9lLkM6dO4fExES3nZ/I38TFxdnttXXmM6ek16m4uNhqg0dSUpLNm2Fn4yLflZSU5NAcW954w0bO65ggsZCL9gICAmAwGAAAOTk5GDhwoMYReT8mSD6iW7duiuZCEkIgNDQU/fv3x9GjR90aj60LYHl5OSeJJVKRkgTJ0Z4aJc81AbYn8bT3XcAeJP/j6CTENTU1fIC/C+n4vcL7Ae8QERGBiooK/Pzzz7jkkku0DsfrMUHyEUoSpLbD4AYMGICcnBy3xsMESTtRUVEWJ4vV6/VeMTM2qc8dCZK9YXttj23tmUJ7w6kqKysRGxvrUFzk2xxNkCoqKhAXF+fGiKitjkPm3e3QoUMYNGiQx85Hlt19991YvXo1jh8/jj59+mgdjtdjguQjEhMT7V5wysvLWwsp9O7dG7m5uaqcu66uDmFhYZ3isZWw8YLnXtae++Bs5V2XuxKk8vJyu9vZSnKUTGTN4VP+JT4+3qEiDRUVFWxQ86Bz5861K7rkbpwmxDtkZmbi5MmTHk+QlQoKCkJTU5PWYbRiguQjlLTInTp1qnWm++Dg4Nbxpq6yNOO2veM3NDR0SqpIPRkZGRYni+UcSF1XeHg4amtrra53JkGKi4tTlCCxwYMcodPpbJZ+76i8vJzvLxcFBwfbbahocfr06dZ7BU8oLCxEamqqx85H1v3qV7/y2iq3KSkpKC4u1jqMVkyQfIS9Hhvg/Jder169VD+3tfkSbD14yYcy3ctaaWUmSF2XvRa/mpoah6vYKR1iV1lZiZiYGIeOTf7NkRZqJuCuszcNQFunT5+2+kyhWnQ6XbuEzRt7LPzRTTfdhOnTp2sdhkUpKSkoKirSOoxWTJB8RGxsLCorK21uY6lVyJFWPGvcOaEcOcfaPBNMkPyXEMLhmxClQ+yMRiMCAwOdDY38kCONZEyQXOfI3FOe6EEaM2YMtm3bxsZSLxMYGOi1w1lTU1MtTjCsFSZIPkIIYfeLpuNzAvYmcFSKz7V4H2uTxdbX17NIQxemdius0iF2rmDLMdnDBMl1SkaZtPBE4ZTx48fjhx9+QElJCedEJEXYg0Qek5GRocps1lVVVYiOjlYhIiLyJmFhYWhoaFDlWJYacAwGA3ue/JQjiXFdXR3Cw8PdGE3X50iCBLi/4aKleMuBAwdYoIEUSUpK4jNI5BkxMTGoqqpS5VjWvkyt9Wqx1VgbHM7gv5z9v1fjPRMbG2vxu8bT1bLIezj6vuI1wzUJCQkOJUiekJKSgo0bN2LIkCFah0I+wNrIGK0wQerC1EyQLImOjrY4Fw95RmhoKOrr69stY4JEWrA2FxIniSUl+L3lOkeKNHgqGf3Nb36DtWvXIjEx0SPnI1ITE6QuwtIwOGutumqxVnq8oaEBISEhbjsvnZeZmYlTp061vj537pzXPnxJ6hBCWCy80tTUhODgYKeP6aqkpCSL3wWlpaW8OfJT7BHyLKWt77W1tR4bzti/f39MnDjRI+ciUhsTJB+i0+lgNBotrrNUlUaNHiQppdXWPWsJEh+49YyOkwHn5ORg4MCBGkZE7mat17asrMzpRESN1nv2IJElSt9bTKY8x5NzIAkhsHTpUo+ci0htTJB8iK2SvJbmQAoPD0ddXZ1L56yqqrJa7cZaglReXs6eDA/omCAdOXKECVIXZ+07wJ2JiMlksnsDa6sHiQmSfwoJCUFTU5PWYVAHnpgDicgV3jLklgmSD0lKSrLYSgtYbhVSo1UuNzcXvXv3trjOVg8SEyT36/hQ7pkzZ3jh6+Li4uIsTuzqSiKiZAJae1UsExMTLX4XsEiD/4qMjIRer9c6DOrAkz1IRI6ydo3TAhMkH2KrBKK1ITauZuK2EqSEhASLrdnsQfKMjje2JpMJAQH8SHdlthIkZ4fYBQYG2mzpr6iosDtnirXeApPJBJ1O51Rc5NsiIyNRU1NjdzslPZSknqKiIqSkpGgdBpFFqampXjMXEu+mfEhycrLVBElK6ZaLTG5uLjIzMy2ui4iIQG1tbaflTJCI3MMdPUjx8fE2W+w8MakkdT1RUVGKepCqq6s5z54HNTY2siGNvFZKSgrOnj2rdRgAmCD5FFsJkrvU1tYiMjLS4johhMUeKiZIniWlRFNTE4KCgrQOhdzMWoLkyrBWJQkSi66Qo5QOsWNRH/UEBwejsbHR6no+f0TeTov7XGuYIPkQbxqbaUt1dTWioqK0DsMvJCcno6SkBCdOnEDfvn21DofczNp3gJTS6VbhuLg4q8VfAPYgkXOUDrErLy9ngqSSxMREm3Mhffrpp7jllls8GBGRY2JiYrxmfk0mSD7E2hA6b6n40RbHlHvGJZdcgvfffx9HjhzBgAEDtA6H3CwsLKzT5MCAa98BtqpjAsqeQXI1Bup6lA6xYw+SehITE9sV7umopKSEzx+RV7M2lYUWFCVIQoiJQogcIcRxIcRcC+t7CiG+FULsE0J8J4To3mbd/UKIY+af+9UMns5TUmXKGUajkWOVvdzIkSPRq1cv/PWvf0X//v21DofczB0ND2o9g6TT6dpNVMmiIf7NkSF2HJKtDmvzkQFAXl4e0tPTPRwRkWPUmJ5GLXavXkIIHYBXAFwLYDCASUKIwR02+xuAt6WUFwFYDGCJed94AE8DGA3gEgBPCyHYVKSygoICm198zrbs5ufnIyMjw+Y27CnS3o033ohVq1ZZfVaMuha1P3P2htgpfYi+ZbhnC/YM+DelQ+z4PlFPz549cfr0aYvrXnnlFdx5550ejojIMdaebdeCkua9SwAcl1LmSimbAKwBcFOHbQYD+Nb8981t1l8DYIOUslxKWQFgA4CJroftvyzdHBUWFiItLc3i9tYqzSlhq8R3W97yZvZnnCDWf6j9eYuJiUFlZaXN8ynpCUpNTW1XfciV0uPk+zjEzvPS09ORn5/fafl//vMfXH311fw8EjlASYKUDiCvzet887K2fgHwW/PfbwEQJYRIULgvOUBK2ekGyVYPUkxMDKqqqpw6l5IEyZUEjIhc5+pQNp1OB5PJZHW90oQsLS2tU4LkbOlx8n1hYWGKhsrU1dUhLCzMAxF1fZY+y1VVVTh69CiuvvpqjaIi8k1KrqqWxnN0vGLOAnC5EGIPgMsBFABoVrgvhBAPCiGyhRDZlmZjp/+xlPAUFBRY7UFyJUHKz8+3O2Y5Pj7eZtUcInIvbymrn5qaisLCwtbXTJD8m9KhoEIIDtV2o59++gkTJ3LgDpGjlCRI+QDaPojSHUBh2w2klIVSylullMMAzDMvq1Kyr3nblVLKkVLKkbyg2mapRnxDQ4PVFjhHEqTm5mY0NDS0vjaZTNDpdDb3SUhIaPf8Qn19PUJCQhSdj4gcJ4Ro10rs7qFsSm9eO343MUEi0l52djZGjBihdRhEPidQwTY7AfQTQmTifM/QXQDubruBECIRQLmU0gTgCQCrzKvWA3iuTWGG35jXk5NabkKUlnR2JEH64Ycf8Prrr+PKK69EcXGxonKgCQkJ7XqQiouLkZqaquh8ROS4lqpzCQkJANRJRNRowQ8KCmpXxa6srIzPPJBdfIZVXSEhIWhoaEBoaCgAoLGxkY2WRE6wmyBJKZuFEI/gfLKjA7BKSnlQCLEYQLaUci2AKwAsEUJIAN8DmGret1wI8QzOJ1kAsFhKab1cEtmVnJyMI0eOKN4+NjbWalWbjs6cOYPnnnsOBoMBaWlpiipXJSQkYP/+/a2vi4qKOM8CkRulpKSgqKioXYLk6iTBtm5Snb2BbWpq4o0ZkYf17NkTZ86cQf/+/dHU1ISgoCCtQyLySUp6kCClXAdgXYdlC9r8/SMAH1nZdxX+16NELkpOTsaWLVsUb+9ID1J+fj66d+/u0E1Nxx6koqIi9OrVS/H+ROSYlgRpyJAhAM731IwZM0bjqIjIG/Tq1QsnT55E//79sXv3bg6vI5/jLc8kchY/H9MxIbFXwcqRBMmZrviOc6iwB4nIvVoSpBbuHMpmNBrtPodI5ApvuRnqKjIzM3Hy5EkAwNatWzF27FiNIyJynDcMvWWC5GN0Ol27N05JSQmSkpKsbu/IrMTOXKiCg4NhMBhaX/PBbCL36pggNTU1ITg42KVjWvvs19TUKBpq2/Y4RqPRpVio62Dy43ltvx/Ky8s5xxT5HEfuW92JCZKPy8/Pt1riG/D8BYotzkTuFRkZiZqaGlWPaS2xqaqqcihB6tatG8rKygB4RwsgaUvJe4DvE3UFBARASomcnByb9wZE3ioqKkr1a5wzmCD5oLYXlP379+OCCy7QMBq2EhJ5kjs+b1FRUdDr9Z2WV1dXO5QgtUwWy5teIm3985//xB//+EetwyByWHR0NKqrq7UOgwmSL5JSts6DcuLECfTu3dvlY1ZXVyMqKsrl4/DGiMj3REdHW2yxczRBapks1tGhedQ1CSHcUiGRbKuqqsLQoUNZRZJ8EnuQyGlDhgzBwYMHW1+r0aKcl5eHHj16OLUvL3JEvi0qKspii52zPUi7d+92ufQ4+b6wsDDU19dbXd/U1NQ6Xw+p55FHHsGkSZO0DoPIKexBIqeNHz8e33//PQwGg2pzHOTl5SEjI8OlYzBRIvIstT5z1i5I1dXViImJUXyclJQUnDp1Cp988gluuukmVWIj3xUZGWlx6GYLvV6PyMhID0bkH/r27Wuzui2RN2MPEjmtpZX24MGDqj1/dObMGacTpICAABiNRlRWVrJiDpEHBAYGwmAwoKamRpWhsWoNsQsODsbnn3+OmTNn8tlEsvpsWwsmSETUEXuQyGU7d+7EyJEj7W6npJX57NmzSE1NdSqOuLg4VFZWcg4kIg9JSkpCaWmpamX11RpiBwBffPEFevbs6XJM5PvsVVzU6/WIiIjwYERE5O2sNdh5GhMkH9WnTx9s3rzZ6eeGOjKZTE6X577ggguQnZ3NBInIQ1rmOsnLy1PlM2etxc6ZG1gmR9SCQ+yIyFHWGuw8jQmSjxo/fjwqKioUDWMJCgpqN5mrJa4Mh7n88suxefNmJkhEHtKSIK1btw5XXnmly8ezliCZTCY+y0BO4xA7InJUWFgYJ4ol5/Xp0wfz589XtG1ERARqa2vdFotOp0NkZCSOHj3KBInIA1JSUvDtt9+if//+CA4Odvl43vJQLHUt9obY1dbWMkEiona85flVJkg+SgiBcePGKdrW3jAHNdx+++14++23Hap4RUTOSU5Oxueff4777rtPleNZ62X2lgsV+aaIiAibLcHsQSIib8UEyQ/Y60EyGo0uD6MZMGAALr30Ut5QEXlASEgIPvroI04ESV4tPDzc5rWHCRIReSsmSH4gMjLS7kVKjVLBb775psvHICJlhg4dqurx2LhBagsPD7fbg8QqdkTkjZgg+YGIiAibQ+ycKeVrCW+wiHwXJ3omtTFBIiJfxQTJD9jrQVIrQSIiImqh0+lgNBqtrjcajQgMDPRgREREyjBB8gOe6kEiIt/FHmDyNL7niMgSb/huYILkB+wVaWCCREQdh9ipUbyFyBtudIiIHMWrnx+wV+abCRIRdaRW8Rbyb3y2jYicofV3BxMkP8AeJCKyp2NLP78XyN20vgEiIu9k777VE5gg+YGwsDDU19dbXc8bISIKDAxEU1NT62t+LxARkRaioqJQU1OjaQyKEiQhxEQhRI4Q4rgQYq6F9T2EEJuFEHuEEPuEENeZl/cSQtQLIfaaf15X+xcg++yNAa+pqeFkfUR+Ljo6ut0FiQkSERFpITw83GbDvifYra8phNABeAXArwHkA9gphFgrpTzUZrP5AD6QUr4mhBgMYB2AXuZ1J6SU6s5oSKoymUzQ6XRah0FEGoqOjkZ1dTUSEhIAMEEi92MBByKyJCwszOYcap6gpAfpEgDHpZS5UsomAGsA3NRhGwmg5UoaA6BQvRCJiMjdOg5pYIJERERasDfJtCcoSZDSAeS1eZ1vXtbWQgD3CiHycb73aFqbdZnmoXdbhBDjXQmWnMeHYYnIlpYepBZMkMjdeF0iIku8YYidkgTJUh94x2+1SQDelFJ2B3AdgHeEEAEAzgLoIaUcBmAGgPeEEJ2uuEKIB4UQ2UKI7NLSUsd+AyIicpmlBCkmJkbDiKgrM5lMHGJHRBb5yhC7fAAZbV53R+chdH8A8AEASCl/AhAKIFFK2SilPGdevgvACQD9O55ASrlSSjlSSjmyW7dujv8W5BK24hFRfHw8zp071/qaxVvInerq6hAREaF1GETkhXxliN1OAP2EEJlCiGAAdwFY22GbMwAmAIAQYhDOJ0ilQohu5iIPEEL0BtAPQK5awRMRkTpSU1Nx9uzZ1tcmkwkBAZwJglwTGBiI5ubmTsv1ej0TcCKyyCeG2EkpmwE8AmA9gMM4X63uoBBisRDiRvNmMwH8SQjxC4DVAB6Q57slLgOwz7z8IwAPSynL3fGLkG0cykBEtkRGRmo+MR91PdZagmtra5kgEZFF3jDEzm6ZbwCQUq7D+eILbZctaPP3QwDGWdjvYwAfuxgjERER+aCWBKljwQ/2IBGRNb4yxI66MCkle5eIqBM+m0hqiIiIsNgzqdfr+QwSEVnkE0PsqGsQQsBkMnVazmEORETkLtZagtmDRETWBAcHo6GhQdMYmCD5CWsXKc51QkQtWnqTjUYjdDqdxtFQV8AEiYgc5Q0jm5gg+QlrwxyYIBFRi8jISNTU1KCgoADdu3fXOhzqAqxdezh6gYhs0TpJYoLkfs1tywAAEVpJREFUJyIjI6HX6zstZ4JERC3S0tJQWFiI3NxcZGZmah0OdQHsQSIiZ2j9HCwTJD/BHiQisqdlLqTc3Fz07t1b63CoC2CCRES+iAmSn7A2xwkTJCJqkZaWhrNnzyIvL49D7EgV1hrn6urqEBYWpkFEROQLOMSOPCIiIoJD7IjIptTUVBQWFsJkMrFIA6nCWg8Sp5ggIls4xI48gkPsiMieqKgo1NTUaH5hoq7DGyZ8JCJyFBMkP2GrSENUVJQGERGRt2GLPqktLCzMYoLE9xoR2aL1dwQTJD9hrQepubkZQUFBGkRERN6ID8+TmgICAtgjSUQO0/p7gwmSn7DWg0RE1FZRUREr2BERkV9jguQnrPUgaZ2hE5F36datG+dAIrfjtYeIbOEQO/IInU4Hk8nUabnWb0Ai8i4jR45Enz59tA6DiIj8mNaNKIGanp2IiLzKPffco3UIRETk54QQMJlMCAjQpi+HPUh+ROtsnIiICODoBSKyLTw8HA0NDZqdnwmSn2PSRERERETexNoUAZ7CBImIiIg8xmAwQKfTaR0GEXkxrSeZZoLkx5qamjgHEhEReVRtbS3n2iIim8LDw1FfX6/Z+Zkg+bGqqirExsZqHQYREfkRTkZMRPZwiB1pprKykgkSERF5FBMkIrLH3hC7M2fOuPX8TJD8SEBAQLu5kJggERGRJ7QtCMQhdkRkj70hdqtWrXLr+Zkg+ZGYmBhUVVW1vuYQOyIicrfg4GAYDIbW1+xBIiJ7fGKInRBiohAiRwhxXAgx18L6HkKIzUKIPUKIfUKI69qse8K8X44Q4ho1gyfHxMbGorKysvU1e5CIiMjdwsPDodfrW1/r9XpERERoGBEReTuvr2InhNABeAXAtQAGA5gkhBjcYbP5AD6QUg4DcBeAV837Dja/HgJgIoBXzccjDcTFxaGioqL1NRMkIiJyt7i4uHaNc+xBIiJ7fKGK3SUAjkspc6WUTQDWALipwzYSQLT57zEACs1/vwnAGillo5TyJIDj5uORBiz1IMXExGgYERERdXUdG+eYIBGRPb4wxC4dQF6b1/nmZW0tBHCvECIfwDoA0xzYF0KIB4UQ2UKI7NLSUoWhk6M6XqRqamp4kSIiIreKj49HeXl562smSERkj70hdm0Lv7iDkgRJWFjWMapJAN6UUnYHcB2Ad4QQAQr3hZRypZRypJRyZLdu3RSERM7o2IMEAEJY+i8iIiJSR8fGOVaxIyJ7wsLCNB1iF6hgm3wAGW1ed8f/htC1+APOP2MEKeVPQohQAIkK9yUP6TgOnIiIyN069iA1NTUhODhYw4iIyNvpdDoYjUaL6wwGA4KCgtx6fiU9SDsB9BNCZAohgnG+6MLaDtucATABAIQQgwCEAig1b3eXECJECJEJoB+An9UKnhwTGRmJmpoarcMgIiI/EhcX1y5BcvfQGCLq2urq6txeCdNuD5KUslkI8QiA9QB0AFZJKQ8KIRYDyJZSrgUwE8AbQojHcX4I3QPy/DfgQSHEBwAOAWgGMFVKaTkdJLcTQvDCREREHhUaGorGxkatwyAiH2PtMZC6ujqEh4e79dxKhthBSrkO54svtF22oM3fDwEYZ2XfLABZLsRIRERERER+xFqjvicSJEUTxVLXxAINRERERORLmCARERFRl8LGOSJSQsshdkyQ/FRzczN0Op3WYRARERERdcIhduRxVVVViImJ0ToMIiLyMywWRESuqK2tZYJE7lFZWYnY2FitwyAiIiIiUow9SKS60NBQNDQ0MEEiIiKPCQoKQlNTk9ZhEFEXwASJVBcbG4vKykomSERE5DHx8fGoqKhAbW0twsLCtA6HiHyArSIN7p4olgmSn4mLi0NFRQUTJCIi8pi4uDiUl5cjJycHAwcO1DocIvIBLNJAHtPSg3Tq1CkkJiZqHQ4REfmBlh6kw4cPY9CgQVqHQ0Q+QAgBk8nUaXldXZ3be6KZIPmZuLg4bN++HY2NjUhLS9M6HCIi8gMtPUgnTpxAnz59tA6HiHxAeHg46uvrOy03mUxun6om0K1HJ68TGxuLdevW4euvv9Y6FCIi8hPx8fHIycmB0WhEYCBvPYjIvoiICNTW1rr9eSNL2IPkZ/r3749PP/2UFygiIvKYlh4kIiKlIiIiUFdXp8m5mSD5GSEEIiMjtQ6DiIj8SGxsLMrKytg4R0SKhYeHo7a2ttNyT0w2zQSJiIiI3Eqn0+Ho0aN8/oiIFGsZYqcFJkhERETkdjk5OaxgR0SKcYgdERERdWlBQUEYMGCA1mEQkY+wNsTOE5ggERERkduNGDHC7ZM7ElHXYW2InRDC7edmgkRERERut3z5cq1DICIfwiF2RERE1KWFhIRoHQIR+RAOsSMiIiIiIjJjFTsiIiIiIiKzsLAw1NfXa3JuJkhERERERORVAgICOk0KazKZvKdIgxBiohAiRwhxXAgx18L6fwgh9pp/jgohKtusM7ZZt1bN4ImIiIiIyD/U1dV5pBpmoL0NhBA6AK8A+DWAfAA7hRBrpZSHWraRUj7eZvtpAIa1OUS9lHKoeiETEREREZG/8VSCpKQH6RIAx6WUuVLKJgBrANxkY/tJAFarERwRERERERHgXQlSOoC8Nq/zzcs6EUL0BJAJYFObxaFCiGwhxHYhxM1W9nvQvE12aWmpwtCJiIiIiMhf1NXVISIiwu3nUZIgWXoSSlpYBgB3AfhISmlss6yHlHIkgLsBLBNC9Ol0MClXSilHSilHduvWTUFIRERERETkT7ypBykfQEab190BFFrZ9i50GF4npSw0/5kL4Du0fz6JiIiIiIjILm9KkHYC6CeEyBRCBON8EtSpGp0QYgCAOAA/tVkWJ4QIMf89EcA4AIc67ktERERERGSL1yRIUspmAI8AWA/gMIAPpJQHhRCLhRA3ttl0EoA1sn3B8kEAsoUQvwDYDOD5ttXviIiIiIiIlPCaMt8AIKVcB2Bdh2ULOrxeaGG/bQAudCE+IiIiIiIi7+lBIiIiIiIi0lptbS0TJCIiIiIi8k9CCJhMptbX7EEiIiIiIiK/FR4ejvr6+tbXer0ekZGRbj8vEyQiIiIiIvI6ERERqK2tbX3d3NyMwEBFJRRcwgSJiIiIiIi8Tnh4eLsEyVOYIBERERERkdeJiIhAXV2dx8/LBImIiIiIiLxOxyF2QgiPnJcJEhEREREReZ2OCZKnMEEiIiIiIiKvw2eQiIiIiIiIzNo+gySlhJTSI+dlgkRERERERF6n7RA7vV6PqKgoj5yXCRIREREREXmdtkPsysvLER8f75HzMkEiIiIiIiKv03aIHRMkIiIiIiLya2FhYaivrwcAVFRUMEEiIiIiIiL/JYRoLczAHiQiIiIiIvJ7TJCIiIiIiIg6KC8vR1xcnEfOxQSJiIiIiIi8lpQS9fX1CAsL88j5mCAREREREZFXSkxMRFlZmUfPyQSJiIj+f3v3FyPVWcZx/PsL62KLNiw0NRWILGFL0iupWKn/olRbrEa86MU2acRE09jERmuioeHCWG+0MWoMRkPaGrQKIpJKmhjFttGYNC20VqWldIdCygpKG0TbJkLBx4vzzHKY7MA5MHNYur9P8mbPvPO8c85Jnn1m3zln3zEzM5uSRkZGaLVaje7TEyQzMzMzM5uSFi9ezNjYGJIa26cnSGZmZmZmNiUtXLiQ/fv3N7rPShMkSSsl7ZHUkrRmkue/K+npbM9LOlp6brWksWyre3nwZmZmZmb2xjUwMMDJkyeb3efZAiTNAH4AfBQYB3ZI2hYRz7ZjIuLOUvwdwNLcngN8DVgGBPBkjv1XT8/CzMzMzMysB6pcQboWaEXECxFxHNgErDpD/C3Axty+EdgeEUdyUrQdWHk+B2xmZmZmZtPHiRMnGBwcbGx/Z72CBMwDDpQejwPvmSxQ0juAYeCRM4ydN8m424Db8uExSbsqHJdZr1wONLt+pE1nzjdrkvPNmuR8s75au3ZtZ9eSfuynygRpsiUjokvsKLAlIto3ClYaGxHrgfUAknZGxLIKx2XWE845a5LzzZrkfLMmOd+saZJ29uN1q9xiNw4sKD2eDxzsEjvKqdvr6o41MzMzMzO7oKpMkHYAI5KGJQ1STIK2dQZJWgIMAY+Vun8L3CBpSNIQcEP2mZmZmZmZTTlnvcUuIk5I+gLFxGYGcH9EPCPpbmBnRLQnS7cAmyIiSmOPSPoGxSQL4O6IOHKWXa6vfRZm58c5Z01yvlmTnG/WJOebNa0vOafSfMbMzMzMzGxaq/RFsWZmZmZmZtOBJ0hmZmZmZmapbxMkSfdLOtz5nUaS7pC0R9Izku4p9d8lqZXP3VjqX5l9LUlrSv3Dkh6XNCbpF7mAhE1Tvcg3SQskPSppd8Z/sRQ/R9L2zLftueiITVO9qm/53AxJf5b0UKnP9c0m9PD9dLakLZKeyzp3Xfa7vtlpephzd2bsLkkbJb05+13jbEKdfJM0N/9We1XSuo74d0n6W+bi9yUp++vXuIjoSwM+CFwD7Cr1fRj4PTAzH1+RP68G/gLMpPii2b0UC0LMyO1FwGDGXJ1jNgOjuf0j4PZ+nYvb1G89yrcrgWsy5q3A86V8uwdYk9trgG9d6HN2u7jzrTTuy8DPgYdKfa5vbhOtV/kGbAA+l9uDwOzcdn1z63nOAfOAfcAlGbcZ+Exp2zXO7VzybRbwfuDzwLqO13kCuI7ie1h/A3ws+2vXuL5dQYqIPwKdK9bdDnwzIo5lzOHsX0WxAt6xiNgHtIBrs7Ui4oWIOA5sAlbljHAFsCXHbwA+1a9zsamvF/kWEYci4qmMfQXYTVHg22M25LbzbZrrUX1D0nzg48C97RdxfbNOvcg3SZdR/BFyX8Yfj4ijpTGubzahVzWOYrXkSyQNAJcCB13jrFOdfIuI1yLiT8B/y8GSrgQui4jHopgJ/YRTeVW7xjX9P0hXAR/Iy6p/kPTu7J8HHCjFjWdft/65wNGIONHRb1ZWN98mSFoILAUez663RcQhgPx5RR+P2y5O55Jv3wO+Cvyv9Lzrm1VRN98WAS8BP85bOu+VNCtjXN+silo5FxF/B74NvAgcAv4dEb/DNc6q6ZZv3cyjyKW2cl7VrnFNT5AGKL5MdjnwFWBzfpKgSWLjHPrNyurmGwCS3gL8CvhSRPyniQO1N4Ra+SbpE8DhiHiy4znXN6uibn0boLiF5YcRsRR4jeJWE7Oq6ta4IYpP7oeBtwOzJN3aLb4/h2wXsW751k1P86rpCdI4sDUKT1B8anp59i8oxc0HDp6h/2Vgdl6yLfebldXNNyS9iWJy9LOI2FqK+Wdevm1fxj2M2enq5tv7gE9K2k9x+/AKSQ/g+mbVnMv76XhEtK+Kb6GYMIHrm1VTN+c+AuyLiJci4nVgK/BeXOOsmm75dqb4+aXH5byqXeOaniA9SHHfKZKuovgn0ZeBbcCopJmShoERin+02gGM5Gong8AosC3vLXwUuDlfdzXw60bPxC4GtfItP5m4D9gdEd/peK1tFHkGzjebXK18i4i7ImJ+RCykqG2PRMStrm9WUd18+wdwQNKSHH898Gxuu75ZFXX/hnsRWC7p0nx/vZ7i/dU1zqrolm+TylvnXpG0PPPt05zKq/o1rsrqEufSgI0U95y+TjGr+2ye3APALuApYEUpfi3Fyid7yFUnsv8mitXE9gJrS/2LKH4BW8AvyVUu3KZn60W+UayKEsBfgaez3ZTPzQUeBsby55wLfc5uF3e+dbzehzh9FTvXN7dyfvTq/fSdwM6scQ8CQ9nv+uZ2Wuthzn0deC7H/JRTK5K5xrmdT77tp1jU4dWMb684vCzj9wLrAGV/7RrXHmhmZmZmZjbtNX2LnZmZmZmZ2ZTlCZKZmZmZmVnyBMnMzMzMzCx5gmRmZmZmZpY8QTIzMzMzM0ueIJmZmZmZmSVPkMzMzMzMzNL/Ad/P4zkQ1oyrAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1008x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "spec_err = 1e-2 * np.ones(len(wavelength))\n",
    "\n",
    "# for a single-star model, the format of \"labels\" is [Teff, Logg, Vturb [km/s],\n",
    "#              [C/H], [N/H], [O/H], [Na/H], [Mg/H],\\\n",
    "#              [Al/H], [Si/H], [P/H], [S/H], [K/H],\\\n",
    "#              [Ca/H], [Ti/H], [V/H], [Cr/H], [Mn/H],\\\n",
    "#              [Fe/H], [Co/H], [Ni/H], [Cu/H], [Ge/H],\\\n",
    "#              C12/C13, Vmacro [km/s], radial velocity (RV)\n",
    "real_labels = scaled_labels = [5770, 4.44, 1.0,\\\n",
    "                               0., 0., 0., 0., 0.,\\\n",
    "                               0., 0., 0., 0., 0.,\\\n",
    "                               0., 0., 0., 0., 0.,\\\n",
    "                               0., 0., 0., 0., 0.,\\\n",
    "                               90., 6., 3.] # assuming RV = 3 km/s. \n",
    "\n",
    "# scale the labels (except for RV) the same as it was done during the training of the network\n",
    "scaled_labels[:-1] = (real_labels[:-1] - x_min) / (x_max - x_min) - 0.5\n",
    "print(np.array(scaled_labels).shape)\n",
    "\n",
    "real_spec = spectral_model.get_spectrum_from_neural_net(scaled_labels=scaled_labels[:-1], NN_coeffs=NN_coeffs)\n",
    "real_spec = utils.doppler_shift(wavelength, real_spec, scaled_labels[-1])\n",
    "\n",
    "# zoom in on a small region of the spectrum so we can see what's going on.\n",
    "lambda_min, lambda_max = 16000, 16100# for plotting \n",
    "m = (wavelength < lambda_max) & (wavelength > lambda_min)\n",
    "plt.figure(figsize=(14, 4))\n",
    "plt.plot(wavelength[m], real_spec[m], 'k', lw=0.5)\n",
    "plt.xlim(lambda_min, lambda_max)\n",
    "plt.ylim(0.7, 1.05)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's add some noise to this model spectrum, and then fit it to see if we can recover the labels we put in. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x101ed64dd0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_spec = real_spec + 0.01 * np.random.randn(len(real_spec))\n",
    "\n",
    "# popt: the best-fit labels\n",
    "# pcov: the covariance matrix, from which you can get formal fitting uncertainties\n",
    "# model_spec: the model spectrum corresponding to popt\n",
    "popt, pcov, model_spec = fitting.fit_normalized_spectrum_single_star_model(\\\n",
    "                                norm_spec=data_spec, spec_err=spec_err,\\\n",
    "                                NN_coeffs=NN_coeffs, wavelength=wavelength,\\ \n",
    "                                mask=mask, p0=None)\n",
    "\n",
    "plt.figure(figsize=(14, 4))\n",
    "m = (wavelength < lambda_max) & (wavelength > lambda_min)\n",
    "plt.plot(wavelength[m], data_spec[m], 'k', lw=0.5, label='\"data\" spec')\n",
    "plt.plot(wavelength[m], model_spec[m], 'r--', lw=0.5, label='best-fit model')\n",
    "plt.xlim(lambda_min, lambda_max)\n",
    "plt.legend(loc='best', frameon=False, fontsize=18)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 5.80423982e+03  4.47429523e+00  1.02808134e+00  9.12571816e-03\n",
      "  2.64261923e-02  7.91369557e-02 -9.80682826e-02  1.06031651e-02\n",
      "  2.64754435e-02  8.97618519e-03 -3.19894979e-01 -2.30204863e-02\n",
      " -1.55436534e-02 -7.84262672e-04 -7.72626725e-02 -3.18917216e-03\n",
      " -2.51876435e-02 -1.77773554e-02  1.93352287e-02  1.07392503e-02\n",
      "  8.41148372e-03  1.94582350e-01 -6.91791527e-02  3.22752775e+01\n",
      "  5.10397306e+00  2.98582662e+00]\n"
     ]
    }
   ],
   "source": [
    "# verify that our best-fit labels are close to what we put in. \n",
    "print(popt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we've seen how to generate and fit model spectra, let's download an actual APOGEE spectrum. Here we'll download a \"combined\" spectrum. \n",
    "\n",
    "Note: Here we adopt APOGEE DR14. Edit os.environs in the \"process_spectra\" source codes for a later version of the APOGEE data release. Since our neural net training set was normalized using the DR12 wavelength format, even though the spectrum is from DR14, we will resample it into the DR12 wavelength grid."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16000, 16100)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from The_Payne import process_spectra\n",
    "\n",
    "apogee_id = '2M18513961+4338099' # make sure the apogee id is in the right string format\n",
    "spec, spec_err = process_spectra.get_combined_spectrum_single_object(apogee_id=apogee_id, \n",
    "                    catalog=None, save_local=False)\n",
    "\n",
    "plt.figure(figsize=(14, 4))\n",
    "m = (spec_err < 0.1) & (wavelength < lambda_max) & (wavelength > lambda_min)\n",
    "plt.plot(wavelength[m], spec[m], 'k', lw=0.5)\n",
    "plt.ylim(0.75, 1.05)\n",
    "plt.xlim(lambda_min, lambda_max)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's fit this spectrum with The-Payne-interpolated model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x101dc09f90>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "popt, pcov, best_fit_spec = fitting.fit_normalized_spectrum_single_star_model(norm_spec=spec, \n",
    "        spec_err=spec_err, NN_coeffs=NN_coeffs, wavelength=wavelength, mask=mask, p0=None)\n",
    "\n",
    "plt.figure(figsize=(14, 4))\n",
    "plt.plot(wavelength[m], spec[m], 'k', lw=0.5, label='APOGEE spectrum')\n",
    "plt.plot(wavelength[m], best_fit_spec[m], 'r', lw=0.5, label='Best-fit model')\n",
    "plt.xlim(lambda_min, lambda_max)\n",
    "plt.ylim(0.7, 1.1)\n",
    "plt.legend(loc='best', frameon=False, fontsize=18)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "## 2.Training"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you have a different training grid, you can also train your own neural networks. But do remember to adopt, during spectral fitting, a different set of continuum pixels and/or spectroscopic mask tailored for your need. \n",
    "\n",
    "Note that, this part of the codes requires GPU (CUDA). It will not run if you don't have CUDA installed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from The_Payne import training\n",
    "from The_Payne import utils\n",
    "\n",
    "# load the default training set. Note that, due to the GitHub size limit,\n",
    "# this training set is a small subset of what I used to train the default network \n",
    "training_labels, training_spectra, validation_labels, validation_spectra = utils.load_training_data()\n",
    "# label array unit = [n_spectra, n_labels]\n",
    "# spectra_array unit = [n_spectra, n_pixels]\n",
    "\n",
    "# The validation set is used to independently evaluate how well the neural net\n",
    "# is emulating the spectra. If the network overfits the spectral variation, while \n",
    "# the loss will continue to improve for the training set, the validation set\n",
    "# should exhibit a worsen loss.\n",
    "\n",
    "# the codes outputs a numpy array \"\"NN_normalized_spectra.npz\" \n",
    "# which stores the trained network parameters\n",
    "# and can be used to substitute the default one in the directory neural_nets/\n",
    "# it will also output a numpy array \"training_loss.npz\"\n",
    "# which stores the progression of the training and validation losses\n",
    "\n",
    "training.neural_net(training_labels, training_spectra,\\\n",
    "                    validation_labels, validation_spectra,\\\n",
    "                    num_neurons=300, learning_rate=1e-4,\\\n",
    "                    num_steps=1e4, batch_size=128)\n",
    "\n",
    "# a larger batch_size (e.g. 512) when possible is desirable\n",
    "# here we choose batch_size=128 above because the sample training set is limited in size\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the loss function of the training set and the validation set. The loss function has plateaued after 1e4 steps. We were being conservative and trained 10 times longer. Depending on the scale of your problem, you might want to change the parameter \"num_steps\" above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Loss')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+4MGD6NixI+zt7eHh4YFRo0aZXEU7ePAgevToAW9vb9ja2qJOnTro2bMnjh07ZnBeUlIS/v3vf6Nhw4a6z33w4MG4du2a7pyPPvoIr7/+OgCgY8eOuvd8r9dpMXLeZxqNBnm5ueYeBhEREVG1N3z4cEyfPh0rVqxA//79De5bt24dMjIyilWXvvnmG3h7e2P06NHw9vZGaGgoli5dinbt2uH06dNo2LBhucdx+PBhPPPMM3B1dcWkSZPg7OyMtWvX4uDBg8XOzcrKwrx58/DCCy+gb9++cHBwwLFjx7B06VIcOnQIx48fh7W1NZo2bYq5c+diwoQJ6N+/P/r06QMAcHJyKnUs7733HubPn482bdpg1qxZSE5OxpIlS9ClSxds3boV3bp1Mzj/5MmT2LhxI0aOHImhQ4diz549WLZsGaysrLBo0aJSX+vixYvo1q0bateujXfeeQfe3t6IiorCgQMHcP78eTzxxBMAgMTERLRr1w6RkZEYMWIEmjRpglu3bmHRokXYtWsXTp48ibp162LAgAGIiorC8uXLMWXKFDRq1AgAijXeqHRSymr9p2XLlrIqSUhIkH9162buYRARERE9FLp27SotLS1lZGSkwfE2bdpIGxsbGRsba3A8LS2t2HOcP39eWltby3Hjxhkcb9++vWzYsKHBsSFDhkhLS0uDY61bt5Y2NjbyypUrumNZWVny8ccflwDkp59+qjuel5cnMzIyio1h8eLFEoD85ZdfdMeuXLlS7PFaOTk5EoAcOXKk7lhISIgEIDt16iSzs7N1x2/cuCGdnJykn5+fzMvLM3i8hYWFPH78uMFzd+vWTdrY2Bgdp7558+ZJAPLkyZOlnvfmm29KOzs7ef78eYPjYWFh0sHBweA9LFu2TAKQBw4cKPU5ywvACVlCnqi2FSYhRG8Ave954iwnjUaD3Lw8cw+DiIiIqLiVK4HwcHOPolCDBsCrr97VU4wcORJ79uzBqlWrMHHiRADApUuXcOTIEfTv3x8eHh4G5zs4OABQRYXU1FRkZ2ejZs2a8Pf3x9GjR8v9+rdu3cLx48cxcOBAg0qIra0t3nnnHQwbNszgfAsLC10Diry8PKSmpiI3Nxddu3YFABw9ehT9+vUr9zgA4NdffwUATJw4EdbW1rrjPj4+GDZsGBYuXIhz586hefPmuvs6dOiAVq1aGTxP165dsXPnTkRERCAgIKDE13NxcdG9bpMmTaDRaIqdk5+fjzVr1qBLly6oWbMm4uLidPc5OTnhiSeewM6dOyv0fitLtQ1MUsotALa0atXqdXOPRZ+trS2n5BEREVHVdJfhpCrq168fXF1dsWLFCl1g+v777wEAI0aMKHb+yZMnMWXKFOzfv7/YOh3tmqHyCAsLAwCjwaJJkyZGH7Nu3TrMmzcPZ8+eRU5OjsF9iYmJ5R6DlnY9UGBgYLH7mjZtqhuvfmDy8/Mrdm6NGjUAAPHx8aW+3pAhQ7B69Wp8+umnmDt3Ltq2bYvu3btj0KBBqFu3LgAgKioKSUlJ2L59Ozw9PY0+j42NjQnv7t6ptoGpqrKwsIA09yCIiIiIHhIajQaDBw/GokWLcPjwYQQHB2PVqlXw8fEptl4nPDwcnTp1gru7O/7zn/+gUaNGcHBwgBAC48aNKxZeTKFmewFCiBLv0/e///0PgwYNQps2bfDVV1/Bx8cHGo0G2dnZ6NmzJ/Lz88s9htJeryyWlpYVfj6NRoM9e/bg6NGj2LFjB/bv34+PPvoIU6dOxbp16/Dcc8/pnqN79+6YMGGC0eexsDBvnzoGJnOowA8rEREREVXMyJEjsWjRIqxYsQIJCQmIiorChx9+WCwM/PLLL8jIyMDvv/+Ojh076o5LKREXF6ebYlYe2iYRFy9eLHafsWOrVq2Cvb099u7dazCFzViXPmMhzJSxhISEoH79+gb3/f333wCMV5TuVnBwMIKDgwEAERERaN68OaZMmYLnnnsO3t7ecHJyQmpqKp5++ukyn6u877kysK04EREREVVrjz/+OJo3b47//ve/+OabbyCEwPDhw4udpw1QRSsnixcvNlhbUx61a9dGq1atsGHDBoSGhuqO37lzB19++aXRMVhYWBhUkqSUmD59erFzHR0dAcCg3XhptJ305syZg1y9JSKRkZH44Ycf4Ofnh2bNmpn2xkxg7DOrV68ePDw8dGO2srLC4MGDcfjwYd0aq6JiYmJ0X5f3PVcGVpiIiIiIqNobOXIkxo0bhx07dqBLly5G24P37NkTH3zwAYYMGYKxY8fCxcUFhw4dwu+//w5fX98Kv/b8+fPx1FNPoX379njzzTfh4uKCNWvWGJ3S1r9/f2zatAldu3bFyy+/jDt37mDjxo3IMrKPp7e3Nxo0aIDVq1ejQYMG8PLygpOTE3r27Gl0HE2aNMG7776L+fPno3PnznjxxReRkpKCxYsXIzMzE4sWLarU6W9Tp07F3r170atXL/j6+iI/Px+bNm1CaGgoPvjgA915n332GQ4fPox+/frhpZdeQps2bWBtbY3w8HD89ttvaNOmDb777jsAwBNPPAEhBKZPn47Y2Fg4ODigYcOGBntpVbqS2udVlz9Vra24lFLu6dJFypwccw+DiIiI6KGRkJAgNRqNBCB//PHHEs/bu3evbNeunXR0dJSurq6yZ8+eMiQkxGgLcVPbimuft02bNtLW1lZ6eXnJt956S545c8ZoW/Bvv/1WBgQESFtbW1mrVi05evRoGRMTU6xNuJRSHj58WLZt21ba29tLALrxGGsrrrV48WL52GOPSVtbW+nk5CSfeeYZefDgQYNzSnu8qa29d+3aJQcMGCDr1asnNRqNdHNzk8HBwXL58uUyPz/f4Ny0tDQ5depUGRgYKDUajXR0dJQBAQFy1KhR8tixYwbnLl++XDZu3FhaW1uXOMbyQiltxYWs5utpWrVqJU+cOGHuYRjY3qMHnl2zBnB3N/dQiIiIiIgeekKIk1LKVsbu4xomM7gSG4stq1aZexhERERERFQGBiYz6P7SS4j55x9zD4OIiIiIiMrAwGQGjYKDYZmaau5hEBERERFRGRiYzEC4ucHuzh1zD4OIiIiIiMrAwGQOrq6wzcw09yiIiIiIiKgMDEzm4O4O+4wMc4+CiIiIiIjKwMBkDo6OsMnJMfcoiIiIiIioDAxMREREREREJai2gUkI0VsIsTQ5OdncQyEiIiIiogdUtQ1MUsotUspRLi4u5h4KERERERE9oKptYCIiIiIiIrpbDExmkm9hAbDxAxERERFRlcbAZCZpDg5AdLS5h0FERERERKVgYDKTqNq1gePHzT0MIiIiIiIqBQOTmcT4+CCPgYmIiIiIqEpjYDITe0dH5GRnA7m55h4KERERERGVgIHJTBwcHJDauTOwcaO5h0JERERERCVgYDITR0dHJDZuDJw/b+6hEBERERFRCRiYzMTV1RVJSUmAEOYeChERERERlYCByUzc3NyQmJho7mEQEREREVEpGJjMhIGJiIiIiKjqY2AyE11gsrYGcnKQlJSEXbt2mXtYRERERESkh4HJTHSBqVYt4PZtREdH48SJE+YeFhERERER6bEy9wAeVra2tsjOzgZq1wYiI5FiZaWaQBARERERUZXBCpO5BQUBZ84gOTmZgYmIiIiIqIphYDI3Hx/g5k2kpKQgOTnZ3KMhIiIiIiI9DExVgY0N0mNi4GDBbwcRERERUVXCNUxmlJ6ejr///htNBg1Cl1694FqjhrmHREREREREeljSMKPBgwdj1apV+PncOWx76ik4Wlqae0hERERERKSn2gYmIURvIcTSqrwuqEWLFrC2tsZPP/2Ek7m5EJySR0RERERUpVTbK3Qp5RYp5SgXFxdzD6VUQghER0cjPDwc2TY2kFU44BERERERPWyqbWB6UNSqVQuNGzfG7du3cSsgANlbt5p7SEREREREVKBSA5MQwk0I4VCZz1ndPfbYY+jfvz8cHByQFRiI7L/+AqQE8vPNPTQiIiIioodeuQOTEOIpIcTnQgg3vWNeQog/AcQBSBBCzK/MQVZnbdu2Ra9evdC4cWPU8PBAuqUl8OWXwNChQHq6uYdHRERERPRQq0iFaRyAflLKRL1jcwF0BBAKIB7AeCHEi5UwvofGhAkT4OnpifAWLYDffwcmTgQ2bzb3sIiIiIiIHmoVCUyPATiovSGEsAPQH8AfUsrGABoDuAHgjUoZ4UOiWbNm8PT0RISdHbBqFdCsGRASYu5hERERERE91CoSmLwA3NK7HQxAA2AlAEgpUwFshQpOVA6enp6IjY0FvLwAIYAHfV+muDi1HouIiIiI6AFVkcB0B4Cd3u2OACSA/XrHUgC438W4Hko1atRAfHx84QGNBsjMBLKygJkzzTewipo/HwgLM/coiIiIiIgqrCKB6RqArnq3XwBwRUoZqXesLlQDCCoHS0tL5Ot3x2vSBLh4EfjnH2DHDvMNrKLS0oADB8w9CiIiIiKiCqtIYPoBQJAQ4qgQ4gCAIABripzzOIDLdzu4h17z5sCGDSo0eXurtU3Z2eYelenc3IArV8w9CiIiIiKiCqtIYPoWwDoArQC0h1qvNFt7pxDiCQCPAthXCeN7qOXWqQO0bg18+y3wyitAaCiwZIm5h1U+FtwbmYiIiIgeXOW+mpVS5kgpBwNwA+Aipewjpbyjd0oYgBYAvq6kMT5U7O3tsW/fPgDA4MGDgd69AVdXoGdPYNo0ICpKrWt6UFhZATk5ZZ93+/a9HwsRERERUTlV+Nf/UsqUgo54RY/HSSnPSimT725oD6eJEyfijz/+QHx8PI4fP46EpCQ1LU/rlVeARYvUND2t+HggIqL4ky1ZAtwqaGi4Z8+9G/T588CvvxY/LgTQuLFag6UvJ6dwXABw8yYwYIDx505KAvTXdQFATEzxY0RERERE90C5A5MQwk0I0UQIYVvk+HAhxCYhxJqCaXlUQRqNBidOnMDYsWOxZ88ew2ltjRoBvr7A1KmFoWHmTOCHH4q38L5yBVi7Vn394YdAXt69GfDNm8YDGwAEBQHnzgEnT6rbUVHA7t3ApEnqdloaMH060LkzkJysOgJOmABs3aruHzMG+PNP9XVKivp7xgzg4EHg+nV1OzxcBckzZwpfV/t6RERERER3oSIVppkAjuo/VggxDsB3AHoDGAhgnxCiSaWM8CHUo0cPfPrppxg4cCAu6leStPr1A55/HvjrL1U5atVKVWL69jU8z9ERSExUISQ6GogsaGR49Wrx55wzp+KBKiZG/SlKSiAgQHXKe/NNNZYxY1TA69ABOHxYVcs+/BDo1UudN2cOMG6cClWHDwPPPgts366ea9Ik4LPPAFtb4L33gA8+UGFtzhxg8WLVFANQoWz4cODaNTWNUd/Roypoxsaq9ytlyXtF3cuqHBERERE9ECoSmNoD2C2l1F9IMwFAJIBOAF4sOPbuXY7todW6dWvMmDEDPj4+yMnJQU5ODmbPno2QkJDCkzp3Bv7zH+DAAVwOCsL+WrVU5Sk/H5g1C1i6VAUBCws1Ze6559SeSDk5aj1UVhagfb5z51SY+eqrkgeVkACkGs7AzMrKUl/ExAB37hien59fuPnuwoXAvHnAqFHAp58Cn38OvP46cOyYCi116wJPPKGmHnp4APXrq/s//hgYMgTo0UO911q1gNxcwMEBWL5cNcMYMkSFMDs74JFHVBg8dAh46SVgwQK13is5GfjkExXAJk9WQXPIEOD0aWDKFBWuhg1TG+1qRUersPXXX3fxnbwLnHJIREREVCVYVeAxdQDs1t4oqCTVBTBRSnmw4NgAqPBEFdS5c2fd15MmTULPnj1x8OBBBAYGqoO1aiH7t99gY2uLyD17cN7GBp0CA1UFRaNR09RsbAAvL2DTJmDQIODCBcDaGhg6FHjjDVWdWboU+PFHFQ6+/FKFBg+P4gP67TfV0nzECABARkYG5syZg48//lhNq3NwMDw/NRVwdlZfC6G6/Xl7A02bFp7zzjuFX1taqiDl6qpuN2mi9p6ysAC6dlVBKDBQhaaYGKBZs8JxaV974EAVyHJy1Ka5VlYqqM2eDbz6qqpgbdwIPPmkqlT98Yd6r598oqb7ffMN0L27Cm4BAapq9eOPQNu2FbG2DOQAACAASURBVPsm3o0PP1TBl4iIiIjMqiIVJjsAWXq32wOQAHbpHbsKFazoLuXm5qJ+/fp48sknERlZuDdwUlISJk2eDABISEhATEyMusj/8ENVLalTRwWUgADgyBE1be/6dWDvXhVUlixR4eGzz1QnPgcHoE8fFSIyM9X0vZgYFSays9VjL11SL56VhbTUVGRkZKjbQhQf+M6dgItL4W1bWxVISuPhoUKOlv7arddfB9q1U1W04ODC4/pBzdUVePllVT3SPk+TJiooNmoEjBypxrRtG/DMM8CaNSo8WViox6akAKtXA7Vrq6pcQABgbw+kpxcfa3a2qpCVpaLTHC9evHdrzoiIiIjIZBWpMEUCCNC73R1ACoCzesfcADxAva+rrqeffhqBgYEQRULJtWvXcP78eQBAfHw8EhMTgYAA3HRwQE5KCnz79FEBwM1NrReytFTT5nJy1NomQIWYpUsLnzQwUK0N+vNPNUWvfXugZk3VgOHOHcDPT1VdwsKQ17w50tPT1TS/osLC1BS6Tz+9Vx9LyZo3N7zdrJmqUOmrVUv97eurmlJoDRsG+PioUKX9vJ9+Wq1l6t3b8DnCw4F169RUwqK2blVrsgDgX/8Cfvml8DM3VXS0mkro7l6+xxERERFRpapIhWkvgH8JId4SQrwG4DkAv0sp9Rdd+AO4URkDfNh17doV3t7eutuyoEFBeHg4NBoNNm/ejPj4eDg7O2NfSAiWNm+Omzdvqgv/Jk1UOPj4Y/XgsWN1U+qKmj17tgoJ77wDeHqqYLV0qarKaCsp2ml8lpaw3r4dDf/5R3W003bI27pVff3776qK869/3bPPxWR16hhO/dP300+GVaymTVWlST+cPvGE8UrStWvqszDm888L957y8lKvU17x8aqRhzEHDxq/j+ueiIiIiCpdRQLTLABpABYAWAo1PW+q9k4hhBeAzgAOV8L4SE+9evVw44bKoeHh4WjdujXGjBmD5ORkaDQajBkzBn9fvIjk5CJbYGkDQN26gL+/0ef+S9vc4Nln1Tqn1q2BFi1UZcrDQ1U7AHXcyQkxrVuj2dWralpb8+ZAly5q+tq336rqi5+ful0GWVKHuvtBu8aqNJaWxqcchoUVrrfSJ6XahPfaNbW2KzDQ9E15Dx0qnP7n4FByYNqxw7BBR2SkmiL46qumvU5JsrPv7vFERERE1VC5A5OU8hqAQADjAbwNoKmU8rLeKfUBLASwsjIGSIWCgoJ00/CSkpIwbNgwvPfee4iIiEBCQgK8vLwQGRlZPDCZ4PJlvW9h8+bA44+rhgeA6jhXo4b6+l//AsaOxe3AQHz56KOqwcT48WotUNeuKoRoGzKUIT8/XzWNqOq0gUl/TVFUlJquWFR8PNCmjdqs99YtFVJNDYW//KL2zsrKUs+dmGj8PEtL9ScxUU0L/PprYOVKNXUwOlo9Pj6+8Pzr19UxbQv1hQuNP+/06canWBIRERE9xCqyhglSyigARlfwSymPAzh+N4Mi4wIDA7Fw4UK0atUKsbGxaNCgAdq2bYuff/4ZderUwVtvvYUzZ84gqaTKRClu3LiBrKwsaDSawvU6+pWpKVPU19bWgIsL0tLSICyM5O0JEwwbNxQ4f/48mjRpAktLS92x+Ph4REZGYs6cOXjvvfdgYez5iggJCUFYWBh6F11TdC85O6sK2/jxqiGEi4vqAujurtZ22ert4Xz5svr8QkJUlah2bRWekpJUuOncWTWTMFa1unlTBZYaNVSTitK+j8OGqaCUlaW66Q0cqI7t3g0cP67C3f/9H9CggeoS2KIFcPYsMGCAamxRr54aW7166vvl5qbGfuGCqg4SmUtCAuDkpP5bQ0REVAVUZEqejhDCWggRJIToKIRoJoTg/+HuIUdHR6Snp2Px4sWYOXMmAMDPzw92dnZo2bIlnnnmGfznP/8pVmHKy8szmPo2Z84cg/tzc3Ph5eWFW7duqQNCGL+g15Oeng5HY40MbGx064LWrFmDOwX7My1ZsgSffPKJwanR0dFISUnB5cuXVdMKE5w9e1Y3LbEk0dHRJj2XyRo0UNMM69ZVHfj691f7WtWrp0LJzz+rMAIAf/+tKkz5+Spk1qmjqjrLlgGnTqmmGhcuqHM3bADWrwd+/VXdrlVLTeWLjS05MGnXKdWtq5pWDB6svleJiWpD45AQdbEZEKAqgyEhKvDNm6eagOzcqULtunXqtSdPBubOVc9Zp47qzgcAGRmqilZUcrIaX2ny84EVK8rzCRMV2rSp8OfwbrHT5L3z3Xdl/7eAiKiaqFBgEkI4CyEWA0gCcAbAPgCnASQJIRYLIYws7qDKkpeXB9eC9TNeXl6oV68eBg8eDFdXV9ja2iK7YC3K119/jZSUFCxevBgXCi7SpZT47rvvABSuW0pPT0fjxo0N2paX5tixY0hISIBD0b2Xijh69Kgu3NSoUQP29vaq/XmB6OhopKenIzk52eSQc/XqVSQkJJR6zrvvvou4uLjCAHi36tdX7dYbN1Zrkho2VGu2Bg8G5sxRIXH9enXu9esqzEyerDbT1XbkS04ufPyyZSpYffmlCi47d6rugx4eKtRERRUPTFlZ6uJk1KjCYy+/XLivVadO6vkTElRYGjFCBblRo1SQmjVLPWd+vtoI+JNPVDOQYcPU9L9Tp1SjkNhY1W7+/ffV3lzx8eqi8/ZtYNUq4N13ge+/V+fomzwZuHED2LVLfQbr1hne/+efpk9NpLKV1aL/QZaaWnp1tTwehCm/D6qoKOO/VCEiqobKHZiEEM4ADgEYBSAXwAEA/yv4O6fg+MGC86iS1axZs3D/IwBCCEydOrXYeVJKbNiwASdOnMDRo0dxu6DxQHR0NHJycpCYmIhhw4YhPz8faWlpaNSoESIjIxFlwv8At23bhkuXLpUZmOLj4xEeHq673aNHD+zfv193OyoqCi4uLkhNTS0WmI4cOWL0OXNycpCj7UBXgtDQUCxatAgbNmwo452YqH591emue3fD41ZWatrdc8+pqlpWwfZk2urc6tWq2iOlaoDRrx8werR6ng8+ACZOVBvszpihgs+TT6rHhYSo7nypqer26dPACy+oRg96UxoNfPSReq0XX1RhTqNRFbD331eNOvr2VftYaZtzNGyoAl2nTsArr6iq06BBwGOPqal6X34JzJyp/nz2mbo/PBxYvlxN3Zs5U63R2rJFdV90dlYhbf161SERUOEpOVk1s1i/Hli71nDMd+4A27ff5TenitBfM3Y/aJu0VCVff105z5OaWthk5m7p/feHKllGhtronIjoIVCRCtNkqKYP3wKoL6XsIqUcJKXsgsKGD00KzqNK1qlTJ7Ro0cLgWP369Yudd/HiRbz00ks4ceIEcnNzdYEkNDQUvXr1wvLly9G0aVPcvHkT6enp8PPzQ0xMDMaMGaP2VypFfHw8QkNDywxMtra2CA8PR15eHiwsLFC/fn1cv35dd390dDS8vb3h7OxsUHkCVHUsVRsYihBC4GLBlJ2LRqbu1KhRA5s2bUJ0dDSysrJw6tQpg/u10xlN5uEB/PabardekiFD1G+znZwKj3l5qb/d3ICWLdUUOn9/oGdPVZl66ikVxrR7ZXXooCpAiYmFm/5+/bWqBs2YoToYensbTJcMK9qk4cknVeDRev55w/u6dSs+dmdntfapVi1VtQoOVutH3NxUmOrRQwWvkSPV+QMGqAA0Z466cF+4EJg0SVWwPv1UTfFr00YFxs8+U6Hoq6/UNCspVXXkyBF17p49ajNlQK3h0obhefPUeXl56vULpnYauHix8i6sjQkLK6wclmXCBKNdBhMTEws7UJr6mqaoikGgyL+zCktLK/n7mpWlNtY2RX6+6R0qqfzS0zklj4geGhUJTP0AHJFSjpVSGsybkFImSynHAfgLwAuVMUAy1LRpUwwZMqTM8y5duoS2bdvi0qVLaNeuHaKjoxEWFobly5ejT58+2Lt3L95++21cunQJaWlp8PHxQVJSEqSU2F7Gb/2TkpJw8+ZNWJexKLt27dqIjIxEXFwcPD094erqarC+Kjk5Gc7OzvDy8ipWYbp9+zauXr1qcEx/Hdabb76J/Px8zJgxw+B4Xl4emjZtCg8PDwghEBkZiTVr1uCjjz7SnaNf5TLm0KFDOHtWbx9mIQxDiDGNG6sOgu++W/y+Pn1UGNL3yCOqClRU586qTTigwpe/v2rV3ry5qhB5eBiEsk8ra3PgktasNWumwl7z5oWfwbPPqnVdAwcCb79d+PiFC1VIfOYZVbnq3l01xsjIUBU4Cwtg3z61Tmv3bhXgPv8cOH9ehc3581WIunNHTU0MDi5cL7ZmjeG4PvgAWLIEOHGict6/MefOqbVYxsKasXON/LY9Li5ONx3WJLNnF36dn2+81Xtenpr2WNXc7RTYK1dUoM7IKHlK3q5d6mfIFGlp6udIa98+NTWUKkduLgMTET00KhKY6kGtWSrNnwDqVuC5K40QorcQYmlFWmxXB2FhYfD19cX333+PsWPHIi0tDbt374aLiws6deqErVu3IigoSBeYXF1dkZeXh2bNmuHcuXO654mNjcXXRabauLq6Irbgf5S///67wX2JiYm6Rg8WFhaIi4vDiRMnUEu7lgfAsmXLAKi24kIIuLu7F1uX5OzsrAtMmZmZWLt2LeLj4+Hh4QEAiImJwYULF3D58mWk6F0UxcTEwM/PD/Pnz4elpSViYmJw6dIlrF27Fnfu3IGUUteavSQRERHFKzem6NzZaIdANGyopu6ZoksXNcUNUA0inn3WsH25v39hi3cA586dQ25ubvnHWhmCg423Vl+6VFXPBg5UjSe0Uw0fe0wFn88/Bz78UJ0jhApdgYEqkHl5qfuffVZNJfz2W2DMGMOKSn6+mt4XEaGmB54+rdZumcrURgDXrqnvaVnPnZurnrNIlRRQU0jvmBK4tC5dKvx63z7Vah5Qa0W0Uz6Tk1WouBeSk1VlsLyys+9+etby5Wo6noNDyRWmmzdNryqmpKjQpHX5slp/R6XbscO0IOTmdv+noj7IYmK41x3RA6wigSkDgFcZ53gWnGc2UsotUspRLtqpTQ8RKSVSU1PhXLAxqyioHty4cQNffPEFrK2tIYSAh4eHrvGCtuOdEEJ3PgCsXLkSl/Qv4gC4u7vDyckJ1tbWmDdvnsEF++rVq3FC77f+L7zwAubMmYOaBRfWMTEx+Oabb3D27Fm4u7vrXrPoBrYNGzbUBaYzZ85g69atCA8Ph6+vL1JSUvCvf/0Lf/75J1JTU3XrswDg1q1bqFOnDgIDA+Hp6YnLly8jKSkJzs7OCA8PR2ZmJjIyMpBZyrSepKQk3RTB+76xrpWVWr9UkrZt1UV8gaioKMTfh4uW6Ohog7VzJqtXr7BNfb9+quGFsXVYL76oAuKwYWpqYJs26njNmobVr+Rk4ORJNT3w5ZdVWPrpJ9V0Ij7ecFrYhx8WNkdITVXhLTNTTfH76y91Qf3eeyqUGfs+JyWpFuulBSYpVdWjfXujF5l5CQlwLs/0ucuXC8cSGlpYbdy4sfC9JSWpyt29CMo3bxZ2cTTVpUuqlX0ZU3nLlJCg3puNTeHUzKIiIw2rRqUpel5cXNmVYlIVX1Oa8FhZFXbtpLJt3qyqqET0QKpIYDoOYIAQ4hFjdwohGgJ4EdyLyWyklMV+q6298BdFpl4JIZCcnAwHBwddxUeftiGE/kW5EAINGzaEvb09zp07ZzCd7p9//sH169eRn58PCwsLdOnSBXPmzMGjjz6qO6dRo0bYsmULxowZA0dHR9jY2MDT0xNrCqZdpaamonbt2ro1TMeOHYOzszOuXbuGBg0aICkpCa1bt8bNmzfh4+OjC0y5ublYvXo1ahdcFNWsWRMXLlyAn58f+vbti9DQUCQkJKBp06aldtBLTk5GTEwMLl68iJUrV5r0md8rkZGRyNe/KHFzU9PhANy5cwdeXl66at+99Pvvv5dvallFCKHWU732Wsnn/PqrauXesWNhG/WxY1XHwnfeUdWptWvVBbeFhboIj4gAjh1T0yBHjlQVvzFjVBfB4GBVtdO2do+NBRYtUmPIylKft7GW91lZKtz8+9/qwnz0aKOByebsWTQ+dsy096+dgqd9Pf1wEB0NaKeoJiaqn4HyVM9zc4Ei1WCjbt82XjX444+Sp9xduAAcOFByyDGVNjCVJiOj+Psu6ZcaKSnq50krO1tNgzXlIn/v3op3dZSysGHLgygx8cEef1WVmsrPlegBVpHANAeAI4DjQohPhRBdhRCPCiGeFEJMgwpKjgDmVuZAyXQDBw40aK4AANevX0ejRo2Kndu2bVvs3r0bjo6OSElJgbGKXOPGjXHx4kX06dMHeQXTmfz9/eHg4ID69evj5s2bunOtrKxw48YNJCcn61qft27dWvf1hAkT0LlzZ9y8eRMajQbOzs5wcXHRbborpURkZCRq166NvLw85ObmIiYmBp6enrrA5Orqirp16yI2NhYtW7bUhZ/o6Gh4enri8ccfB6ACU0hICEaMGIHBgwdj2rRpWL9+PYKCgooHEUBXKcvKykJubi5u375ttKnE/bRy5UqjFaSbN29iwoQJaN68uS4wpaamYp+p6ztQevWs6PtOSEgwmPpoFtr9eby81JoxQFWW/PxUhSciQt2uUQOYPl0FpgEDVEOGKVNU44oXXlBrpRYvVtMDg4LUmquTJ9XzLVumLnifeEL9Ntjd3XiF6ddfVUMOjUZVx+rWNTolz+bKFVhop9KVJSVFhTftLyCkLKyuZWer93fypNq/y9e3fIHp1i3TOhLevm28qcK5cyU3mkhIUOGxvNX8tDTDphpJSepPaUHF3t5wOmJOjmp/b0xKihqT/vM5OZV90Xr5MrByZfGujqYKD1dr8R5UiYmGUxlNdfly5Y+lIkJDzT0C41JTK/a5ElGVUO7AJKXcDeBNABoAHwD4A8AFALsATAHgAOAtKeWuShwnlUOTJk2wZMkSg2Mff/wxhg4dWuzc9u3bY+fOnXB0dERiYiLc3d2LVZkaNWqESZMm4dFHH8XFixdhZWWFjz76CPb29ujcuTN27dqFAwcOACjcXDc+Pl435U5fw4YN4evri6SC3yQ7OTnpQlqbNm1w5MgRXWDq2bMnpk2bBjc3N9SsWRNnz56Fk5MT3NzcUK9ePfj6+uKJJ57A+vXrsXPnTsTFxSEgIEA3/po1a+Ly5cvo3Lkz/P398fzzz+PAgQMICgrC2bNn0atXLxw5ckTXwnzOnDm6oCmlRFxcXJmb5JalaOOK8kpNTS3WtXDBggWYNWsWTp8+jRYtWuimD4aEhGDXLsN/dlJKzJ07F3F660uys7MRFRWF8ePHAwA2b95c7HU//vhj3X5eQBUITBYWam+n5GQ1hc7bWx3XTvezslJVkMceK+wE2KgR8Oij6qK8Qwd18fzCC+qiOzhY7YXl76/OtbRUVZj0dFWxGjVKXfS6u6sLsFdeUa+7b5/q8Ld7t7pYf6Sg0O7qalgdOXgQ2LwZFomJSNFoTJs+l5Sk9tAyErxgba1e78wZ1ba9QYPi1ZgbN1SoNCYiwrT1JrdvF362+uLiVJD7++/i98XHA6GhyDLWxKQ0oaGq5byWnV3ZFSYLC8MAFB1d8jSnlBQVrvWr7c7OZU/p271bVSorGgD27bu33RvvtaQk0yohUhp+L4psiG42X3xh7hEYxwoT0QOtQhvXSimXAGgE4D8ANgLYU/D3FACNpJTfVtoIqUKc9NtbA2hQMI2rKFdXVzg6OsLR0RF2dnZwc3ODlBJLlizRVSDq16+PsWPH4uWXX8bevXvh7u6OWrVqwdPTE927d8fSpUsREhKCmJgYXfiJiIiAj4+P0df09fVFQEAAAOgqTADQu3dvbN68GaGhofD390f79u3x3HPPYezYsejWrZuu0924ceNQu3Zt9O3bFx06dMC1a9dw6tQpxMXFoYZeQwRvb29kZGTo1mVNmjQJV65cQXBwMKytreHj44Pw8HCcOXMGgGqgoL8eKjY21mjFDShendHe3rFjB0aPHq07/sorr+iCR2JiYpmb7haVmpqKtCK/lYyPj0dycjKmTZuGTp064dSpU9i+fTsuX76MuLg4SCmxdOlSxMfH48KFCzh+/LhBcNu9ezfmz5+PjRs3Ii0tDWPHji3WOOLatWu6PbRWr15t/sDk7Q306lUYlox19bO1Lfz6vfdUdz2tzz83PFcINT1P2+lRo1FT1rp0KTwnMFAFpqNH1bqxnj3VBc+NG8CCBeq2dj2ZEKpz3R9/qNt//gkcPAir+HhEurqqqYNaJXW4S0xUgSk6WlWwgoLUBemyZer5pVSvnZlZOCVPP0yvX6/a3wOFjSi00wSvX1dT0ZYtK73pRXq68aYLyckqdE2ZUvwxWVlAcjJCtZscmyo01LBRhDZ0ltSx0VjlKTKy5PU2KSlqzZL+vx8Xl7LDTGys6kZZUVevGm+GUlR0tGqrX9VYWpa/EpKXB/zzj/paSvN2z7t27f68zj//lG8d4cMamLhhOVUTFQpMACClvC6lnCGl7C+lfKbg7xlSygghhIYb1z44unbtCltbW7i7u8Pd3R2Ojo5YuHChbqqbpaUlBg0ahMaNG2PdunW6qX3du3fHU089BW9vb8THx2Px4sUYPnw4ANWau23btkZfr3Hjxhg3bhwAIDAwEE2bNgUAWFtbw8rKSldhAtR0Pjs7O/j7++vWQbm6ukIIgaCgIHh7e2PHjh3IzMw06KIHAA4ODvD19dXdFkLo1ku98cYbqF27NuLi4nQd8VJSUhAVFaULWAkJCfDw8Cg2dQ9QUwu1Tp8+rdsk9+zZs3B1ddVtruvo6IjfCi5iN2/eXK4pc4DxwGRhYYGffvoJTz31FIKCgrBq1SocOXIEu3fvhoeHh64ZxpYtW3DkyBEMGTIEt27d0r2Po0ePIiwsDG5ubtiyZQt69OiBiIgIXNO70HB3d0dowdSW9evXw8LCAhXtOJmTk1Pu5hnTp09HXl4e8vPz1efXp4/6YypnZ8M9sYzRnzYVEKDalGs7+mk5OanGEu3aFTawGDBAVamee07to6XVsKGaMvfxxyp4fP45Lr7+OsI8PNQaKu1Upx491LqfvDzgf/8DZs1SFxXawHTrlmpKMWCAaoCwaZMKO5aWKiw1a6YC044d6rm0YmLU+DZuVPtf/fJL4Wd2/bqaNrh1a2HzCO3aquxstf5L3+uvG671cXVVLdxLquZYWCBBoykMcBkZZbdjDw0tDBZSFq/SFRUebvh5A+qzKmhYo6O9WE9OLj0wzZ2rLni1jUG0tJ91RZn62EWLTG+RXlmK/rfM2L9LU6YtAirYaoN8YqLq5AioiuOXX979WCuqvIFJyopturxmTfk7Q1aXKXkLFph+7syZ96ZBDdF9VuHAVIZvAZTvV+lkNgsWLIAQAm5ubnBzc4OHhwfs7OxwpcjFkZWVFb799ls888wzumNCCHz33XfIyclBXl4e3N3dERAQgPDwcGhKmKJjaWmpqwT5+vrCz89Pd5+vry/CwsKKTQssjbYDX1xcnEFgAlQg09ewYUPdVEFnZ2dERETA0tIS48ePR7169RBV8D99KSVyc3NRs2ZNg+lsqampuHnzJtatW6e7oNdu5AsA6enpaNKkCa5fvw4pJYKDg3Gs4ML0/PnzBhUsU6SkpJS6kbClpSVmzJiBt99+GyEhIbCyssKuXbswfvx4XLt2DdevX0dwcDCuXr2KHj166N5XcHAw+vTpgwMHDqBDhw7YuXMnpk+frnvNFi1a6N7T+fPn4eTkVOEK09dff23Qqt4UZ8+exYULF7Bs2TL89NNPqnNe0QvjytS2LTBxYvG28EKoSoVe8C5K97l8+KHawPeFF3QXolmWloi2sQH++19V9XrvPbWmat48NfXr1Cng8ceB8ePV2qSGDVWYKvglAj78EBg0SAWQrl1VYBk7VgWBI0dUIEpOVuGjYUNVhUtOVoFx7Vr199WrKrxoNOp97NmjqkXz56tW3vv3qzVKaWkq8AihxqDfWt/GRr1GvXrGW5rXrIk4a+vC+zZsAFatKv0zz8hQ1azERLXxs5eX+qxLCtdnzqj9wAC1bmrfPlVhqlPH8LwBAwobL9SqVXiRKoRhYDp2TK1V2rz57htWGFPaLwny89Ufi7v8X3B5u9SNHWv4mO+/L94V0dHR9EqIi4v6nsXFFVYGY2ONTys9eLCw6+PdKO1zzc1VVdjySE8HDh8ufry06iWg3nN5/ptoahAtyciRVSNw5eYa38+stOnAxhrnED1g7lVgAgDTr3jJrCwK/qfdvn17+Pj4wNvbG0OGDMHff/8NGxsbg3ObNWtW7PFFjw0aNAiLFi2q0FiCg4Mr1MpbSomEhIRi66YmTZpkcLt///6wt7cHoKoo0dHRuoYYr732mm49UJ06dRAZGQkXFxeDoHD48GF88cUXqFOnDsLCwjBlyhSkpqbqpq8BKpSFhYUhPT1d1349Ozsbtra2BuHLFPn5+cUqTEUNHz4cNWrUMPjMrayskJOTg9zcXHh6euLQoUOoWbMmDh06BCsrK0yYMAEdOnTAP//8g4YNG+L48eNIT09HUlISIiIi0LJlS124S0lJQVJSkq7hh7Ex5uXlITIyEt9+W3w27vXr13Hq1CmcPHkSMTExJlXZ3NzccOjQIdy+fRuNGjW69+3da9YsvrmwVrNmhtP9ipg5c2bx82fNAqCqa9k5OWoN1PLl6qLp5ZeB775Tv4V/4gm1we9bb6kg4+amqkMDBqjnEkI1tKhXT1W5Xn5ZTRv08VHt1AMC1FqbDRtU10AAePVVtbdVSgowYwawZUthIHBxUSFq8WI1Bmtr4Icf1Ia577yjugPa2qpgUjTkpqSozZnPnSu8yNZuKFyzJmIsLdXF59q1KqQdP15YvdFeeOblqff911+F+0qtXasCnaenerytrQp3GRnqIlt7XkiImiYJgoZsDwAAIABJREFUqLVP332nLlpr1SqsZiUlqXFeuqSCgYuLukhNSlLvW7uGKTVV7fN1/bqqUp0+XdiF8G4JoRqPJCSU/Jv1kBAViu/253r06OLVnORk9TNkzKlThk0RzpwpXjW0szPe+EOfdtweHiogxcWpf0M5OSosGQtMp09XTlvtf//beFDUTgV0cDAMwAsWlL5vWVyc8fHu2qWm45YkPt749M47dwzX5mnZ25f9uZZlxYq7e3xl0H6/9eXlqX+PxiQlcb8uqhbuZWCiB0z79u3h4uKCHj16YOzYsfjrr7+KVWxKkpmZaVBRKqm6VJZHH30Ur7/+erkf5+npicjISFgVqRD4axf1F3jxxRd11SvthrmDBw/GqFGj0LJlS+Tl5ekqQ1euXIGzs7PBVLR//vkH+/fvx/PPP48LFy4gJCQEqampuhboUkr4+fnh6tWrSEhIgJubG5588kls27YNGo0G+fn5ulBmCm0TDVMEBwfD0tISnp6eAFSjhueffx4WFhb4559/MGbMGKxbtw4NGjSAEAK1a9dGVlYWvL29ceLECcyZMwezZ89GWFgY/Pz84Ovri127dsHf37/U6tLkyZOxYsUKXL161WglydnZGTt37sTcuXNx+PDh4gHDiNq1ayMqKgpSSnh7e+sqf2bx0ksl3iWl1DUwMUa3ce3LL6sL6blzVUixtFQVGG3IadQIGDFCXWh7eKjAoNWgATBunHrM88+rY1ZWKjQ9+qjaNychwWBDYwBqyuHjj6vOgh4e6vHW1qql+qxZqpIwbJgaR+vW6oInIEA9T//+6qJe27VOSlXJefVVFbCmTVMXfx99pF77pZeQlJ+vgsikSerYm2+qfZ2iotQmxXl5qpojpQo0s2eri9tLl1RbeDc39bWfn3qtiAgVMl96CfjgA/Wc2uB69qz6XG7eVIFp4kTgjTdU1W3CBNUWPD1dvcfwcHWx/thjhRWm0NDChh9OTuo9FUyrLfINVn+vWKFCHqCmQhYNQqmp6vWystQY/f3Vhf2oUerzL9op8c8/DfZTq7DatYtfvF65ot4voN6rfnfD+vXV1EotCwvDoDh2bMlryCZPLtx4NStL/Yx6eqoL6Ph49bOjDR/GNvCOiVE/Cxs3lvttGjh82Hil6uBBFY4aNDCs5Bw4UHrVKS7OeOUnNFQ1QSlJYqLxCtPNm8CPPxY/rp2+WFE+Pne/OXTR6YcljUf7s27M7dsqgOpvJZCQUHJ3QgYmqiYYmKgYCwsLWFpaolWrVhg0aJBJj4mMjESdolNjKvjaXfQX3puoU6dOum53pqpRowaSkpLQs2dPWGsX/xcIDAyEj48PXFxckJiYiEMFvzGMi4tD06ZN0bt3b1y6dAkRERFISEjQbfwLqCmCkZGRuopXhw4dMG3aNPTr1w95eXl46623dOf+/fffujVUxjg4OJRZYdJXq1Yt3dqx+fPn44mCTXA9PT3RunVrHDt2TNdwo1atWvD19YWXlxdiYmJQt25d2NjY4Pz582jSpAkGDBiA7du3o1mzZnB3d4eUEvn5+QZrkm7fvo06derg2rVriIiIQEpKCvLy8jB79mxs2rQJ2dnZEEJAo9Fg9uzZuHTpEk6ePKlbT3X69GlEljBNRxtsfX19DdZX3XcjR5Z4V25ubqnfn5ycHKNr4AAUDzgTJqgAYUxJF7ENGqhGD0Y6YKJdO/W4Dh1UxcrdXYWLsnTurKpYKSkq6B0/rsJWv37qt+TffqumMK5erYJKTg7w7LNIzs1Vv1kfNEhdHD/2mGqMMXYs8O67KswcPKgqWcOHq7HVrq321OrTR60Ji4xUj/HxUc8/dKjqjti9u5q2qJWVpaYrjhmjqm8BASr8bd2qpkRGRACtWqnANHkysHChqpq5uKjPa9Qo1eEwO1uFm9271QWw/gWktbV6f3Fx6mLw66+BTz5Rlb1t2wqnR23ZosLf+PHqYtLTU1Xivv1WvY9Fi4qvk4qJURUZCwvja72MrQFKSAD+85/C29qx2tmpZiOzZ6vb2uYLgPreLVyozr19WwVj/e5/2il1Wvv2FU59jYkxDAyXL6v3DhQ2B/H0VJ9PXJwK79rpeF5G9rZPS1PTPIs2YCkvKyvjlarz59X7LbpHWWKiqiRevqzCvLYxi1ZsrPq3UZSUpU/J02iMV5hu3zYeHu4mLFVWhT0hwXD64bJl6t9KUUZmCujcvq1+AfPrr4U/HzExJQdCjYaBiaoFBiYq0fjx4+Gsv/FjKSwsLColMFVUUFBQuV/f3d0dDkUuUBMTE/H000/D2toaX3zxhW7D3NWrV+vOmTBhAgICAnDnzh3ExcUhMjISTk5OuhBhYWGBvLw8JCYmws3NDVZWVpg9ezYCAwN164q01qxZo2sYYYw2MG3duhXnz58HUHzzYX3Dhw/XhSTt1EMAGDNmDKysrFC3bl00LtjDqEaNGhg6dCicnZ1168geeeQRnD9/HnZ2dnB1dUVYWBiCgoJ0Ux1Xr16NdevWYeDAgdi6dSv++OMPdOvWDTY2NggNDYWPjw8iIyPxxx9/YN26ddi9ezcAYMWKFahbty7Onj2Lxx57DOHh4cjOzsaCBQuwYcOGYhUk/Y2WGzT4f/bePC7Kcv//f93DMjNsA8O+7wKihoIbau6ZHs3KJft66pSlx1+2WJ06ZVmePtmemmZ5yjb1nMoyzWwvNRU3FDBEAQFZB2Zg9oGBAeb+/XF1XzP3LCyGqZ37+Xj4EO659xnget2v9/V6J/BKHq8m2tvbYe6hzKarq8tJjA8oHh4k3IGb8+SKO+4gA/fgYOf5Pq5ISyMDyJtuInOLnnmGOFX/3/9nW2fsWCIUli4l7hAAXWcnESMPP2wTmRMmkPNbtIgMzq6/ni/+Fi0ibpanJxn4v/gicXyio8mAbMwYsnziRFvgQ0wMKVOUSoHsbNJHa/lyIhDffJM4PC+9RPpjZWSQOVoVFeT6fXzI93fdReZ8GQxERPj7k3vJCR6AOF6nT5MBNsMQAdfVZROp69aR3lYnTxIhtWwZCQ7hxIK3N0k69PEhDkZbGxENL79sG1hypaAtLcSZaGkh7tMrrxAhZTCQ8sniYjLvrbaWiBWWJaWEISFk3ppSSYTjkSNElCQlEceyqopc08aNRMimpNiSDA8dIusB5Lq0WuLmzZtHln3wgS2iW6cj97yqiqQx7t1LBJN9SR4Xia9W2x4GHDpErmfHDlIOWVhI7kFjI/DDD7bPwbFjpATOEcdUUbWalLG6EiRNTWTf9j3KuPertpaI3CeeIPfInpYWkrzp6Bp6ePSc+ugoNjkaG0mASX9EjmNEuyMmk/sQG1fbdXW5T5W0r3C4eNG1W+eqfQBHUxN58NLYaNtWpSKfdVeVAMHBNsHUz4ealwUuQVRAoJ8IgklgQAgJCXEbI/5HIBKJsKmfSUdcKqA9b7zxBiZMmAAACAsLg0wmQ1VVFaqrq2G1WmG1WpGZmQlPT0+wLAsvLy80NjYiLi4OFy9epE5TUlISTp06Rfc/Y8YMACTunUsF5Pan/W1C7KZNm5waxvr6+qK1tRVHjx5FcXEx2tranMoO7fFwk9B1229lZS+88AKCgoIAEDEyY8YMMAyDcePGAQBycnIglUrp61qtFoMHD8YDDzwAhmFQWFiIqqoqZGRk4OjRozh37hzS0tJw/fXXY//+/fD398e5c+dgsViQlJSErVu30s8FwzCoq6vDDTfcgBtuuAGfffYZbrrpJhw7dgy33nornn76aacmvSzLIiEhATWunoJeBfQmmDo7O3t8v/5QJk0iAqSvTJhABM1nn5FBuT2jRpESPpEIuP56dHd3E4dJrSaDz2nT+Ov7+JCABcekQ09PvoCaOpX8Hx1NBIcrx+3ee4GZM3s+dy7BTSwmQoJ7KME5bitWEMEVFUVExB13EMfpiSdsZZJyOREBX35JSia5J+geHkQYaTREzDQ2kvswciQZ9Nu7KzNmkNLEhQvJfKOlS4kDxM0NXbyYlKi9/jop4Zs3j3w9fjyZK7ZqFRm8vv8+OZfVq8kxP/mEOFqxscTJ++tfyXu1cycRMLGxZP7NiRPEuczOJiJx8GByzdXVRJD+7W/keu65h4ij5GRbwIlWS3qa/d//kXPIzCQlmGlpxK1buJCIy61byf0dOpSU+3V3E3H02mukfPHgQdJIGiAD9LlzSfjJV1/ZBq4HD5IySo7iYvJv+nR+0EFZGUmy5Abq3IMUpZLMa4qKIgKJEzIXLxKhp1AQEfVbA3UenNizj0J3FBtaLXEjv/vO9pp9gAjLkvcIIMcaPdrWPqA34WS12j4HJ08SR9IRtdp91P2nnzrPmXrnHSJWHWlo4Kc4KhSuRU55OV9U2F9DUxP5fBmNZHuA3P/Ro8n9dnT/OMGkUJDPYG8UFQ1ME+TNm10v/+YbIp4FBPrJVfKXXOBaZ8SIEVdUMAHgRYj3BS4RsCcCAgJQVVUFg8GAw4cPIzc3l75mMpkQHR0NvV6PhIQE7N+/nzo1c+bMwdSpU7Fo0SLe/latWoX33nsPLMuisrIS6enpqKiogNVqxcWLF7Fr1y7ab6qpqQmenp7o7u5Gd3c3ysvLMX78eNx77739uk57OHfJkZd/K+cZNGgQHn/8cbrc398fwcHBCA4ORk1NDSZNmoQDBw5gwoQJqK2tRVBQEBVcvr6+yMzMxJ49e5CVlQUfHx/s2rWLdxxPT0/MnTsXkyZNwgMPPIDPPvsMnp6eSElJwQcffICCggJMnjwZHh4edD6ZRCJBu+MckKuEjo6OXgXTZXWY+gjLsjhSUUEfBvQLV0+2PTxsTgxIM2Sj1Uqcp4HA15e4RgOFXRInLxI5KYkM/mbNsiXX/dbSAHI5CfCw66uG8HDb/LLly8m23GeTYYCMDHz4zTe4ixOmXNAEYJvXUlRkSw3z8yP/br6ZXLNcbpvz8eSTZOCrUBBxcccdZJvYWCJgjEbitnEwDCkhrKkhy+PiiNjhkgW5UJOAADJwfe45ss3q1UT03X67TbCWlpL5ctOmEWH4+uvEvZNIyD2x56efbGmFgYGkLG7OHLJs7VriWkkk5HxFIiIg5XLiML34IjlfX1/yr7ubCJNHHyXfr15NRMSxY0SkFxURwXzwIBm433kn+cx5eBDBarHYHKannybloo88whchXJNqo5G4iDoduSal0lay2tRE3D9OTDz+OBHN+/YRYfn66+TzzwmzEyfIILyri9yvW28ly+Ljyefnscf4JWvnztlKNS9cIML6zBniaNo1DKef13HjiPBQKsl9mzqVXEdnJ9m+tpasw1FXRz43JSXkfnNw79OuXcQVkkqd52m1tpLPiFZLjnn4MJnn9lvLEGi15B5LpWT5jz+SfU2YQMJgVq4k184w5OfJ35+I3p07yYMObv6bPVoteXji708eBphMxMF1CJ2iHD5MjtcTW7YQ19fx9291NTkH+z59APlMTZzovvxZ4H+ePgkmhmEE/1KgR/7qag7FVY63tzcesp8X4YKAgABUVlYiJiYGP/zwA/71r3/R11pbW5GSkoLKykokJCTgv//9L03lCw8PR2BgIHVzOOLi4miQw6lTp5CTk4Pu7m5UV1fD19cXbb+lObEsiwULFuCmm25Ca2srJBIJ8vPzERsb67TPgYBLShSJRBg8eDBdHh8fTyPgU1JSsHDhQuzevRtpaWmYPn06PRdvb2988MEH8Pf3x4oVK7Blyxbai8qe2NhYJCYmQiKRgGVZxMfH06bKq1evxttvv43o6GgEBASAZdl+BWRcKq+++ioee+wx3rIXX3wRTz75ZK/b9sVhuhoEU0dHB7777rtLE0x9wGKxoI5hiHMzUCxbNnD7ssdePN1+u22AJBLxn4BnZxPXxJ5Jk2zr/zYXkOeCPfooiv79b9fH5bYbPtz5tdGjbV9HRZFBa3c3OdfERP7rCxeS/z/+mH8tABkMc2WXgYE2sWZPZCRJN+TcdZGIbNPVZdvW05OIHu548+a57y/l40PcOYCIg+BgMvD8zamGvz9xv+rryflGRpJ7MW0aOc6SJURMTplChGNcHHG27ryT3Ie//52UMf7wAynFCwoiQuqvfyVpkNOm2Zyj668nYvPgQTIojowkIrez0zZwTkggzl15Obm206eJw7ZpExmsv/QSES/XXUcEU1sbuVdvvknEm0RCylMnTSICpL6eOE//+Q8pVfz0U1LGumsXOS+ZjIijiAgSlHLqFHHkYmOJuDp5koi+vDziAHZ3ExHz5ptE5O7YQe7XyJFECH/wAXFgxGIy9y4tzSayurrIPczKIsdJSyPO1S23kGPu32/rF+fhQVwrxyAJlYqch1pNxCbDEOFntZL7mpNDhF94OBGJ331Hzn/ECBKeYrUSZ3HiRFJWGhBABKxaTT779fXE1TUYyP0dNIhck68veSjg40PKdPfsIaLmyy/5wTsGAzmvQ4fIZ6y5mXzG7EVYRwd5z6uryWeztZV8nZlJzsOV67d5M/n8c5/lnigtJdfh6KQDpCSZK2XlKC8n1zlQdHeTf/aCkkvldDV/UGBA6GtJHnMJ/wQErnocU/QcEYvFUCgUtGmufXlVVFQUTZCLj4/H/v37kZycTF9/5ZVXnOZIASSAQaVS0XK2wYMH44svvsCgQYMQHR0NhUIBjUaDiooK+Pv7Q6fTISAgABaLBZs3b8bc/jRw/Z3ceOON1IV76qmnaBPhQYMGIS4uDv527kNkZCT8/Pzw7LPPYvbs2bj55pud9rdy5UqaoLhjxw7efKyAgAB89dVXWLNmDWQyGcLDwy+LOHTk1KlTTrHlfe0bda04TO3t7ZfVpbNYLDB1d9uS564VenqaHBJic5s4hgyxRZu7IisL6oHolePrSwbe3Dm6+gzdfrvrsAKOgAC+u2BPcLDztWdl2QTTO+/whWBfm/EyDAkYCQuzOZOPPkoGvqGhxPXhjuvjQ5y9qChSCjlqFElf7OoipaNhYWSg3tREzu2hh4iIAsi+ly8nzox9mV1wMBEo775Lyg0ffZQsT0y0hWuMHUuE1dKlRIQtWkRE2quvEpG8bRtxsoYOJYJlyRIizNavJyJBJiOiiuuL9vDDZHAukRBhySVSlpYSR+if/ySCZc4cIlaOHSPO29y5RKCVlpKfm9tvJ0JswQIi4KZOJS7j22+T6wkJIWLoww9tomrUKOLOiETkHPbuJdfQ2UlKaV9/ncwbUypJ2ENJCRGRBgP57HR22iLaX3yRLFepyNy/M2fIPeAeGtxyCxEp8+cT0RoVRd6bDRtsAqyyktyPHTuIYNRqybrcw7PYWOJ+nT5Njvfhh2QblYq4ZD/8QN7TnBziVq1bR9w4+9/HZ8+Sz84ddxB36803ycMDe8rKSBkxN9ft8GHiOHFzqBiGHPfpp21z1/z8iKAEnF0+R06eJPeXZcm1cX8/jEYiYktKbOuazXSep0s4gQnYHE2z2bk/mj1ffw18/jn5Wq0m58uJ9b5yuVt1uMJVSMo1RJ8EE8uyokv49ztapQsIXD10dXXhpptuwp3cH+vfiI6ORnJyMsRiMQICAhAaGspLy8vNzXUZ0BASEoInn3wSaWlp8PDwQEZGBjZt2oSZM2di8uTJeO+991BSUoJ7770XYWFhyM/Px5gxY/DII48gJiaGF+ZwuZk9e7ZTRPxzzz2HsB6eYv3973+nSYuOZGdn06+5tD57tm3bhq6uLgQEBCAsLIzOAeOCNC4HKpWKOnscCoXCpUPmSHt7u9t5Y8DVJZg6XKWxDRAWi6VP9+t/gZ5i5vvFQDwp5mLo+8KGDTZXyF1a46WQkUGcjrvv7jmcBCAuxdq1trl2IhEp5WIYIkjsHdKZM12Xi8rlZCBs7w7On08G4gBxEGbPBm68kYRr2FdHzJtHROOxY2Tf06cTURMdbZvbBhCBlZJCBsYMY3tQ4OFh6022bRsZ1IeEEIE2eDD5/oEHyDbp6cQlGjqUXOfkybY5d6NGke8fe8zW2DoigrwvYjH5/qmniPizWomwO3KEnPezzxKR5u9PHLxp08hrwcHEZQoLI8JtxAhyr2pqyLW1txNhxjlM27YRMfmPfxDhYzYTAeLpSURQRAQpjxw2jLhMALkP06YRwVRcTLYLDCSC1dvbJpg++YQIws5OIpwmTCBidOdO4qRxcK0LPvmEuL+HDpGglQceINvv20fWycsj9+r0afJ6URH57F+4QIJKTpywOcgtLeQevvIKORY3dy42lrhG9fXEHTOZiIP41lu2pE2A9DK7eJEIxsWLSclkfj65f3l5RPTv2WO7hqNHiUi1n59bXk5+3t5+mzhkzzxDrm3xYvL6c88Rx9P+d3ZFhW2/hYXkHAByXp9+SgSg/Xw0tdqWttncTIQ4N6/OaiU/E+5oabGVLv/668DN+Zo58/f3IruCCHOYBAR6ISYmhkZ127NgwQKYzWZs/a1h3wKu2WgvhIaGQqPR0DLGgIAAzJ49GyEhIQgODkZiYiJeeuklvPrqq8jMzMQbb7yB7OzsS+5tNdBcTsGWlJSEuLg4yGQypKenI/C3p8dcI+G4uDhs2bIFS5YscWqqfKmo1Wro9XqeG6jX66HX63ud49bR0dHj+9LV1UXnoa1bt86p9O+PgnOYKisreS7oQGGxWGhZ5+UkPz8f3d3dGNOf8Io/mAETTH80A/Tz5BIPDyIE+oLjz5yLBys94uXlHC4SFEQEiOM5OYpSkYgMxLm5XL0RGUlcHPvSR+4BSl9+X7trWbBmDfmfG0A7zltlGCJcsrPJ19nZRJDOnk2uwd55HDqUiMN33iFCqauLlIcNGkTek9deI4NuX18ywN6+nQiS118nIgIgCZQPPGDr4ZWVRUSwY4jGyy8T9wcg+373XSIGwsKIABGLSWmcVGoryUxLI/fRaiVlkNzvEQ8Psh73wGn+fOI2VVTY5t+p1UQQDR9OxMWGDeSYs2YRcfrRR0TgtLYSUbl2LUltfOwxIi5zc0npr05HPv8WC3G9tm4lrmJICLmvaWm29+TBB0lp6bZtxJXMyyPr/fILEXbff08cqP/3/0iZ5pEj5HhHjtg+l//+N3F/9+whYuuRR4gwjYggbmBgIHnP164lzqxSSe6D0UhcRs4ZPHyYnDNXKioWE+forbdIuEV0NFnvo4/IdZ44Qd4XzgGbNImElSQlke0+/5wIq4ICcr4PPUTcO6uVfKa4cVBBAXkvJk8mTtv99xPXlQsW+uYbcr+VSuLMrl9P3oPUVHLtXF/BawxBMAkI9IK7AWZAQAB8fHxo9Pqzzz7bp/1FRUXhL3/5C2/Z5t+eXjEMg7/+9a94/vnn6dyepUuXXjVi6Y8gKysLgYGBiImJoUEiUVFRNI3w4MGDmDt3LiIjI9HV1YXJkydj7dq1uP766+k+3n//fSxZsqRPx+vo6IBer0eUXflVa2trnwRTe3s7xFwzVRdwgslkMuGoff+T34nVau2XQOEE07p16/Dmm2/2GE1/KVgslgETsD2hVqud+lodPHjwknq3XS6uWcEk0H9WrbKJiiuBWGwbxNq1vuARFMQP67AP/klM5KfJPfQQEWlisS0sBCDlgoBtHo5YbGsibY/9w7S//Y2Uy3FBGjIZ+d9gsDVttv+5FYn4zZxHjrTF09s3O7cPgwkOtq3j6UkG8vfeS66ZYYhLwqVlcr/z5swh4oP7e7F4MREN06cT18ZsJmW377xDBvXcdmIxEVY5Obb+XBMnEgFYWkqOf/iwbU6fyUTKGtvbiXh4+GHi1MyYQe57cjIRel9/bSvHlMvJe7BmDXl97lyyv9+mBGDfPuJ2TZxIygdXryb7e/hh4h7V15N5ZnV1RPwcOECET0sLETfPP0+O/eqrpNUAV0KYlEQE5Lp1xJkrKiLz5b74gnxuuObh6enk3v7nP+Rcv/2WCMQbbiAu17Fj5H2eM4eI55AQUtL6yivkfV+5kjhWu3eTf9u3k3s2EE28/wAEwSQg0Av2ZWSOeHp6IrIvzUDtiI2NxcqVK3tc56WXXqKOx7UYqPF7uOOOO5zEQGhoKJS//ZG6ePEiWlpaEBkZibKyMtxwww2oq6uj65rNZuTn5/dZMMlkMhgc+qn4+flBp9P1KgTa29v7JGYNBgMuuGq2eYn861//4gWQ9IbZbEZHRwd0Oh0MBgNk3OBlgPijBFN7eztvvllHRwf27dt31QkmlmUHXJQKXIXYPaS5qtmzp2/pbwxDHA2WJSV/vwc/PzI3zZHXXuvb9rNm9f+YPj6kjJArkeRKTO1x/D01dSpxqRISiJPCuWiO7SASE4mbMnOmLZEwJ4fMs1u4kIgQTjAuWULcr/nziUgRiYgzlJBAxNXf/kbWS0mxOV45OcSFSUiwuaqOY4/Zs20tHj77jJT/cb3tgoNJyevUqcQl41y8BQuIKPT0JHOItm8njhY3X3HTJpICaTIRofbDD0TcxMcTwcf9nVm5kryfq1aRz8j06SSQ5dFHibh84glyPQUFtublv/5Krq2lhYjQhAQiwGbPJte2fj1xBgXBJCDw5+AJV7/07Vi6dOmAH9NVYML/Cq7mBIWFhaG4uJjGjLf8luxUVFSEKVOmoKCggK6r1WrR2trap2OZzWZERERAbzcZ1Wq1IigoCN9++y3Ky8txO/d01QUdHR0Qi8U9DpAZhoHBYADLsuju7u5xzlNf6W8jX85h6u7uhkqluqYFkz16vf6qi5xvbW1FZ2fnH3I/BAT6RH/DcxiGlMdda6xaRZyZ/nLPPeSavb3dBxMkJxP358ABmxATicjcqyVL+HOvACJgvv/eJtC4klD79ifR0aTEkCsN9fXt+33nIvXt/+54eRGHiXvg6O1NRBTXt+7RR4mLZp+6ef/95LhxcUQoL1lC3MW//pVfVhoRQVywt98mpYzXXUeOd+utRFy/9hoRZdy+Fyyw9Umz7+Pn4UFKDh9/nDhPfXyweTUgCCYBgd9Jln3pgsBlITQ0FM3NzWhubsbgwYOpYCotLcWCBQvw888/03Vyr9a9AAAgAElEQVS1Wi1MfUwq02g0SExM5Akmg8GAmJgYlJSU9Cos2tvbIZPJep3LZDQaMXToUNTW1va7X5grGrjGnX2EE0ydnZ1QqVRI7Ut0bj/4IwWTvTA1GAxXnWDq6uqC2WwWBJOAwB8NV7rWXziBMWaMe8GSmkrK31y5Vpxj5EhvvwM8PMgxL2X+pztXxnFfa9favo6J4fduA/j3jGGIyxQd7eywAUR8ffYZcZY8PYmTJRaTkA/H9aOiSImfK7ZvJ+tzDa2vES7/LF0BAQGB34mPjw/a2tqwfv16zJs3Dy0tLfj4448RFxcHb29v3ryW/jhMrgSTTqdDfHw8SkpKoLZPNnJBR0cHgoKCeowWB8jAPjMzk1c6eKlYrVY0OjabBGl0/DkXNesAl+YnkUguS2+rgRZM7u4nV1rIodfrL2v636VwNTdaFhAQ6AGRyH1CpFxuK08bSB5+eOD3+XuIj3ctlgCyfNAgsg5gmw+Xm+t6fXdloO72f5UjCCYBAYFrAq1Wi9DQUEycOBFqtRpVVVW8qHer1Yrt27ejtraWRnl3d3f3OAlfrVYjISGBCqaHHnoI27ZtQ3x8PJRKpVN/Jkfa29sRGBjYo2BiWRYGgwFxcXEDEghgNBpdOmjNzc2orKx0e54SiYT2AHOkxbF5ZT+xWCwQi8VOgQyXyurVq10ud+wndbU5TF1dXfD39+9VQAPk89pkHwMsICDwvwcXd3+t8OGHl+aI/Qn437xqAQGBa46SkhJkZWXBy8sLnZ2ddJDOcf78eXz11Vc4cuQIbXh78uRJvP3227z9FBcXY/78+ejo6IBGo0F8fDyMRiMAIDAwEMePH0dcXJzLhDyz2cxzo/riMDEMA71eP2CCSa/XuxRyRqMRWq3W5Tb2gqm5udnp9eeee+53nZPFYoGPjw+6uCaQvxN3TpxjPymDwXBV9X/q6OiATCbrk2BSKBTYtm3bH3BWfywdHR34j7vENgEBgWub8PArfQZXDEEwCQgIXBNIpdIe54udP38eCxYswOnTpxEUFIRDhw7h1KlTTvN98vPzkZWVhZqaGmg0GoSGhtJABpFIhDvvvBMJCQl0nk9eXh4A4IUXXsCqVauwatUqnDt3DkDfHCaxWAy1Wt1vwXT27Fn88ssvTsv1er3LuVUmk6lXweTt7Y3Ozk58/PHHNBmwu7sbF7kmiJeIxWKBr6/vgAmm+vp6l8sdBZNer4e/q+alVwju89AX10upVF5V7lh/uXjxosv3u7W1FeXl5VfgjAQEBAQuH4JgEhAQuCa44447IP+tIaOr0q/y8nJMnz6dhjVs374dn3/+Od3mwIED+OWXX1BdXY3c3FwoFApoNBr6elNTEyIjI7Fo0SIMGjQIs2bNgtVqxSOPPAKAPDn39/eHp6cn9u7dC8A2QHaMJbfH29sbLS0tiImJ4blTrtBoNPTr4uJi1NTUOK2j1+sRFBSE7u5u3nKj0eh2/5xg4oTh3r17UVFRQffX21yt3uAEU3/cnmXLlrkVmvX19S5dNJZleYN0g8FA+6ANJCzL4vXXX+/3dh0dHb0KaA6lUtmn9a5WPv/8c5flnWaz+ZoWggICAgKuEASTgIDANcFtt91Gv/b29obFYuG9zomXhIQEiEQiREREICsrC56enujs7MSZM2ewf/9+sCyL6OhoKBQKtLW1Qfpb6lFtbS1i7ZpQLlu2DGq1GkajkYoTT09P+Pv70zlEVqsVvr6+uOmmm5wEjP25tra2QiwWu12H4+6776ZisKqqykkAFRQUYOfOnYiMjHQabBuNRrdNdNvb2+l1ikQitLa20vlOWq3Wyfk6cOBAj+fpCFeS1x/BZDab3ZaktbS0OL2/AJyi2/V6/WURTGq1GqWlpf3ejhNMfREMKpXqmhYWJpPJ5Vw6x3lmAgICAn8GBMEkICBwzTF27FheKltGRgaN9c7OzgbDMBCJRHjjjTcwaNAglJeXQ6vVoqqqChkZGYiKioJCoQBgG4TX1dXxBBMABAcHY968eWhoaADDMBg0aBAiIyN5SWhDhw7F4sWLncQN16g2MzMTZ86ccbqG9vZ2vPXWW7xlTU1NOPVb48TW1lYYDAbU1dVh3W/xrHV1dfj2228RFRWF1tZWngNlNBrdlqe1t7dDLBaDYRgMGTIEmZmZqKqqAkAEk9QhKve9995zuR9HDh48iCNHjmD//v1OJXknT57scdukpCSXaX9ms9ltcIKjYHKcxzZQ1NfXo62tzeVrPfXA6kuJJodSqYTnNZoWBbgXTILDJCAg8GdEEEwCAgLXHOPHj8fChQvp97fddhtWrVoFAHjmmWcglUrpoHXo0KE4e/YsAODZZ5/F7bffznOJOGpraxEXF8db9tRTT2HKlCk4d+4cvLy8MGXKFMyePRsjRozA6dOnwTAMwsPDkZOTQ8vpWJbFrl27sHz5clgsFuTm5rocWKrVapw4cYK37Prrr8dPP/0EADTc4qeffkJ+fj7dpqWlBdHR0WhtbcWzzz5LtzWZTPD39wfLsjh//jxvvx0dHfDy8gLDMJg+fTqWLFlCBYFGo0FQUBCvBK6srMzJDXvzzTedruH9999HbW0tSktLnRymN954o8dSP3eNftVqNWJjY90Klt/Drl27+rReXV2d27jyDRs2uH2tPyV5bW1t8HUXYXwZ+fe//z0g++lJMF2pqPfeUi0FBAQELhVBMAkICFxzSCQSZGZmunwtOTkZfn5+1O1ITU1FWVkZACAlJcXlQJ1hGOh0OqfyLolEgpSUFBw5cgQREREICwtDfHw8xo0bh61bt+L48eMAALlcDo1Gg19++QWlpaX45z//iZqaGqhUKkgkEtx///1Ox1Sr1Ty3wmq1ws/PjwoFbvBXWVmJW2+9FYcOHYJarcakSZMQGRmJtrY2nD59GgBxPTi3RaVSYYmL7ukikQhSqRRBQUFITU2lpX9arRbR0dEwm82oq6vDxo0b0dTU5BQgsXXrVqe5Y2VlZVCpVKiurnaaw6RWq1FcXOx0HgARFpxDaLVasWPHDt52MTExAzq/p7OzE7t378a+ffucXtu9e7fTsvr6epcpiQARBFwcuOMcs/6U5F0JNBoNDh06NCD7utocpvPnz7vtQ/ZH0t3d/T8ZetHX3ncCAtcqgmASEBD40+Hr60sHvN7e3igtLUV0dDRvHfvBf3BwMC9wwZ6YmBicOHECkZGRdJlMJsO7775LxY1cLodWq8WWLVuwa9curFixAvfffz8tObv33nvptidOnMDBgwd5wQbd3d1oampCQEAAvLy8oFAoaBiFSCTC/PnzsWfPHrS2tuKll16CXC5HXV0dFAoFLBYLZs2aBZPJBIZhcPbsWaSlpVFXjUMikcDHx4d+7+3tDbPZDK1Wi+TkZGi1Wvz8888oLS2FRCLh3Y/Ozk5oNBqnErqmpibU1NSgvb3dqSQvKSnJrWDSarU0+v3MmTP4+uuv6WsajQYxMTED6jC1trYiLy8PDQ0NTvOsuAAPe5RKJcLCwlzuy2w203JOxzj2/pTkXQkqKytphP7vxWKxuBwkuxNMJpOp1zl8vweDwfC70x4HAp1O12cn88/E2rVrr/QpCAhcVgTBJCAg8KfDz88PoaGh9PsVK1Zg6dKlvHWkUikdlEdERLiNxGYYBuPHj+cJJoAEQHz44YcAgKCgICiVSshkMuzbtw+zZ8/G+PHjnUqTFAoFnnzySWzfvh0FBQWIiIhAZ2cnvv/+e2zevBlyuRwTJ07Ec889h9zfuqczDAOGYSCTydDd3Y20tDT4+PigsLAQgwYNwqFDhzB+/HhUV1dDJpPh+PHjWL16tdPTdolEwpurNHHiRBw6dAgajQZJSUnQarWoqKiASCRCSkoKNBoNmpqawLIsKioqMHXqVKf5O56enlCpVIiOjoaPjw+OHj2Kd999FwAQEhLiMkXtP//5D08w/fTTTwgLC8P69evR0tLyux0mV+l2bW1tNNzCca6ZqyRCq9XqtmSwo6ODCsfCwkJUV1dTAdWfkjx3+7+cVFZW0rl+vxdfX1+3DpOrFMudO3c6ifiBpL293amFwJXgf3UOV09JoQICfwYEwSQgIPCnw9fXl+cQjBs3zmmAOm3aNPp1REQEBg0a5HZ/q1atctkDKikpCQBxmPLy8jBt2jTU1NQgPj4eERER8PLycjpmbm4uKisrUVRUhOuuuw719fU4deoUiouLqWAqLCxEVlYWWltbqSvEJftx13fy5EmMHDkStbW1GDNmDGprazF9+nR88sknSEpKgo+PD77//nt6bLFYzBNMY8aMQX5+Pjo6OhAREYH6+np4eHigvb0dGRkZUKvVeP3119HW1oaSkhLMnDkTBw4cwIMPPoiuri50dnYiKiqKJ5i+//57mEwmnD17lsaYNzQ0UGeBZVns3r0bOp0OgYGBAEhYRVBQEKqrq3H8+HHU1NQgPT39kh2mb7/9ln79zTffAAB10gwGg5Ng4lw6ezjnz9XAPzg4GAqFAlqtFvX19SgoKEBxcTF0Oh1Onjx5VZfkVVVV0c8sADQ3N1/yfCM/Pz+3gskxRAQgyYcD5W65or29nf58XEn+DILp/fff7/c2vbVMEBC41hEEk4CAwJ+O7OxsjB07ttd1ONdpyJAhmDNnjtt1ucAEdwQFBeHIkSPIzMzE9OnT4e3tjfDwcF6SHwAsXLgQI0aMQFxcHIqKijBmzBiUl5ejs7MTLS0tCAoKAsMw+Pnnn+Ht7Y3GxkbExMQAABISEmhDV19fX1RUVGDIkCGor6/H8OHDIRKJkJmZic2bN4NhGDz++OO8xreuSvI4Vy06OhqvvvoqFi9ejMTERAwePBgajQYtLS3QaDQ4f/48ZsyYgQ8//BA5OTk4f/48tFotBg0aBIvFgoSEBHh5ecFoNGLp0qV47bXXEBkZicjISNx8882oq6vD2bNnodPpUF9fz3OYuPvq4+ODgoIC6PV6RERE9Mthsp/sf+bMGXR1daGlpQUPPvggOjo60NbWBo1GAx8fH6eBnU6n4w3kuXI8hmGwZs0aJ9EUHByMlpYWVFZWIjY2Fnq9HjqdDhcuXMDOnTshk8ncnvvRo0f7fE2XA64fF8fevXtRVFR0SftyJ5jsI+zt+SME05UKm7DHnWD6+uuv3TZkvtroLeHSEZZlBYdJ4E+PIJgEBAT+dISFhbmdg8IhEomQmpoKgAietLS0Sz6el5cXVCoVUlJSsGXLFrrMsYwPIEl4Dz74IJqbm5GdnY2CggJ4eXkhICCAzlvy8/MDQEQNN/cqMTGRCh5fX1+MHTsW/v7+qKurQ0pKCj3W9ddfT48VHR1Ny5QcHSZ7MjIy8PPPP2PQoEGYMGECRo8eDbVaTf91dnZCJpPhs88+Q25uLrZu3YoXXngBaWlpCA0NxdKlS2m/Kz8/P8jlckRGRuLGG29EZmYmiouLsXXrViiVShgMBp5g4vD29qYDXq5cct++fb0KJ/ueXO3t7fDx8UFzczOOHj2Khx56CN9++y3MZjPa2togk8l4PadYloVYLOYN5I8fP46xY8eCZVk0NTXBYDBAqVTS10UiEaxWKy5cuIAhQ4ZApVJBp9PR3k0+Pj4unSkA2L59e4/XcrlxTJFzjKbvD2Kx2KUw6MlhciWwBgpHMXilMJvNLj+zNTU1TkEqVyuuSml7oru7+7KkWgoIXE0IgklAQEBgABg5ciS8vLx4Ls5dd93ltF5ISAhycnIwePBgBAYGYv/+/cjOzkZSUpKTiJDJZFQwxcbGUhEYERGBf/zjH/Dz80NjYyP8/PywYMECp2NNmjQJhw8fBsuyTnOY3HH99dcjMzMTOp0Ora2tUKvVdKA9fPhwJCcn47vvvkNhYSHS0tLo9XAx6ACJY8/NzUVycjJWrFiBwsJCNDQ0QKlU0oAMriTPnsDAQFqGaDab8d133/FK7OzhzkkikVCh1djYiBEjRqCxsRFFRUVYtmwZTp8+jba2NrAsi8jISOowcU/Fo6OjeYKpurqalq1ptVqcP3+ezlUDbI5YZWUlrrvuOjQ0NECr1aKlpQXJyck99oWqq6vjnbsjl9MhcXXPTSZTr4JJqVS6DMbg5tY5YjabeT8DHFar9bI7TFKp9IqntblzmFpbW6/aMBBHmpub+7W+xWK55ssQBQR6QxBMAgICAgPAgw8+6LRszJgxbte/7rrrAJAn9bm5uZg/fz6Cg4N56yQlJVHB5O3tjWeeeYZ+nZaWRvtJMQyD22+/3ekYERERUKlUYBgGqampiI+Pd1rH1aCXc1ECAwNx/vx5RERE8Nbfu3cvwsLCEBUVheXLlwMgjhpX4hccHEwj2iMiIlBYWIi2tjYolUrExMSgqamJumlcQ12WZTFz5kzI5XLaR0sul7ssDyoqKqJ9jOydDnvB1N3dDbFYDJZlYTabIRaLER8fTwXTkiVLoNFokJCQgF9//RW33347urq6aF8qgJTrNTY2upwb09HRAblcDp1Oh/b2dqjVaowfP55ejyu4ZERXDXfb2tpwxx13uNyOg2XZS+41lJ+fj5EjR9L9cNfgKGJaW1t5x2hqanIb1uDqXNw5TP7+/gMimNwl7bW3t2PZsmV4/PHHf/cxfg89CaZrRVT012ESBJPA/wKCYBIQEBAYAG688cZ+rf/KK68AANatW4egoCBMmTLFac7TAw88wHta7xiN7m4eCUdgYCAtQRs2bJhLwdQTwcHByM/PR0pKCm95WloacnJyIJfLaRiGp6eny5KosLAwFBYWIjIyEkqlEoMHD4ZKpYKnpyf8/f0RHh5Or2PIkCF44okn4OPjg7a2NjAMQwMp7HnooYdgMBgQEBAAiURCB2sKhQLZ2dm8+HMupCE6Ohrx8fHQ6XQwm83Iy8tDfn4+4uPjcezYMXh7e9N7xTAMvLy80NLSAoVCAYVCgR9++AHPP/88AOL2NTc38xog63Q6PPHEEwgODnYralQqFSwWC0wmE/z8/HjC6vPPP+81OW/nzp04ePAg/f6rr77qcX17OOeMc++463TkkUceQUVFBf1er9c7OQ4WiwWenp4uj9PV1eXyNVfNoktKSvodFvDUU0+5XN7e3o7Bgwfz0jEBEunfm3O3efNmp2UnT568pFQ/s9nsMnGzJ8FkMpl6dcb+qMh0lmV/l8N0OaPjBQSuJIJgEhAQELgCcA5MT+l8veHv79/jINvDw8PtfBrAfWkYQAa+oaGhOHXqFBITE51ef+SRR3iJa15eXi7L7MRiMVpbWxEZGYnm5makpaXR+Q7+/v6IiIig/ae4c+YcJoZhsHjxYnz++ef46KOPaFlXWVkZ6urqIJPJIBaL0dHRgQMHDqChoYE6TBxyuZwKpri4OBgMBpw5cwaPP/44/v3vfyM+Ph5lZWXIzMzkzTGRSqXo6upCY2MjTCYTDh48iK6uLrAsi2HDhiEuLo7nmrAsi+TkZCocHcuvWJalZVmcYLK//xUVFcjIyHD7fgDA/v37edHZmzdv7pPj9OWXX6K5uRmhoaHw8/PjnbM9Go0GZ8+e5YkYnU7HG0C/8MILMBgMdJ6dI+7Ox9vb26kH1qFDh1BSUtLr+dtz4cIFl4LEVdjEuXPnUFRU1GMggcVicXkOJ06cQFVVVb/ODSCCydWDg55K8p577jnk5eX1uN+XX3653+dyKbS1tfW7NNRisdBrW7NmzWU4KwGBK48gmAQEBASuUfz9/V2KFHt6GlB3dna6dQpkMhmioqLQ0NDg0plyTA50J5gA4m5FRUWhuroaYWFh1LGSy+WIjo6GTCbjzd/iyuxYlkVoaCg0Gg2qqqrwr3/9CzqdDrfccgv2799PHaYLFy7gmWeegUajQXh4OE3EA4jLxgmm4OBgWK1WnDp1Cn/5y1+wYsUKXHfddaioqEB6ejq0Wi0vtU8sFkOhUMDPz493n7Kzs7F8+XL4+flR8Wd/n7mwjXXr1qGoqIg2eQ0JCeEJJgA8x6g3JBIJmpqa6PcVFRVuy6d27dqF6upqWK1WrF27lpYBOpbG2b+HX3/9Ne68806ewNDr9bxr++mnn1BUVOTkkJ07d87pHJqbm1FRUYGuri54eHg4vW40GlFTU4NPP/20j3eAhEe4EkAdHR1OZY4XL17EmDFjehRMer3epZC5cOGC22bWPeGuJLEnh+ncuXO9liuWl5f3+1wuBaPRiICAAF60fm/ul8Vioc6S/edTQODPhCCYBAQEBK5R/Pz8ehVMRqMR/v7+Ll+zWq1OQRMc6enpCAkJQWZmZo9BBhyenp5uz2Xy5MnIzs7GsmXLkJSUhIkTJwIA5s2bh6ysLCfBxDAMr7SLG7wxDAOxWIzbbrsNP/74I3WY/vvf/9LEQ24Qz4m8wMBANDY2YvHixRg5ciTMZjPq6+sRGRmJW2+9lUajh4SE8BwmHx8f2vtKKpVSN41hGHh6ekIul8Pf399lwEF0dDTq6urwzTffYMeOHTh37hyvIa99Sd7rr7+O7u5uMAzDSwp0xGKxICwsjDewDg8PR1lZGR577DGn9U+ePIktW7agrKwMOp2O3kP70jiu9JBLGSwtLcXo0aOdHCb791Umk+GTTz5BamoqT0itW7eOd3yWZXH06FEcO3YMzc3NCAkJcZnSd+zYMezYscPp/C9cuODyPrS0tLgs43PVbFipVCI1NfWSBBPDML0Kpq6uLuzfv5+3zFEw2V+zK8HU1dXl9L66oqGh4bKmDHIYDAaEhITQz+HZs2edmmA7Yj8nr6Wl5bKfo4DAlUAQTAICAgLXKL6+vm4FD4erCG+OwMBAt69NnjwZU6ZMwbhx4/p0Lj05TE8//TRGjRqF8ePHIz4+HpMnTwZgEzcBAQFO51FXV0fno2i1WoSEhAAg7tPQoUPR1dWFgIAAxMTEYNSoUUhPT6cDytmzZ2PIkCH0GhUKBQIDAyESiTBo0CC6L4CIzoiICAQFBUGtVtPlUqkUsbGxaGxsRHp6OrKystDd3Q2RyPZn09/fHzKZzEkIREdH48yZM6ipqYFGo8Gvv/4KtVqN2NhYnmCSSCQ4ceIESkpKkJSUBLlcTgfp5eXlOH/+PN2nRqPhhYK0t7fjuuuuw3vvvYe9e/fSMrUzZ87QCHVvb2/k5+dj+vTpVGQ4luRFRERQV8DT0xMymYwnSPR6PWQyGQAinkaPHo2ff/4Z48ePp+t0dHSgurqavqdeXl7YtGkTSktLodfr0djYiKioKKfPBcMwyM/Pd4qk7uzsxLp167Bx40anbby8vHjR8D3BRf1z1/Pf//7XaR13gsnf37/X3kJGoxF79uzhLXNMCVy5ciXa2tpclmkCoOWqvQkmq9XKKzXtCc6dBdDv/kgGgwFhYWFUMBmNxl7PzWKx0PmX9j9DAgJ/JgTBJCAgIHCN4uHh0WvYRGtrq1tR5Ojs2BMQEIDQ0NA+z53oyWHqjZEjR2L27Nm8ZSNGjKCCSSaTUQdpwYIFkMvlmDx5MgIDAzF8+HA8+OCDSE5OpttOnTqVJ5iUSiV9An733XfzEg3FYjESEhIgl8tRWVlJ74ePjw8VTI8++iiuu+46J/fAz88PAQEBMBqNGDZsGF0eHR2N06dPQyKRIDo6GpWVldRh0uv1tMxPKpXCx8cHhw8fRnp6OoKDg6HRaHDs2DHs378fx44do/tUq9U0WbC7uxsXL15EVlYW5s6dixUrVtBeUC+++CLuuecejBw5Er6+vsjPz8ekSZNoL6nAwEBe2VRkZCQdiDMMg4CAABgMBpSVleHEiRPo7OyEl5cXWJZFaWkphg4dinfffZdXYsf1ouKQSCTYtm0bTpw4QZMGIyMj3caQcwKYQ6vVoqmpCUeOHKEi48svv6TJjY4OU35+vtN+uX1HRERQ0eAYUa9SqaBWq52cn+7ubnrNjY2NbqPtTSaTUyCJ/VwqlmXx448/orGxkRdOYg8nmHpzjyIjI3nz13ri/fffx5kzZwAAy5cvd5o71hMGgwGhoaH0XE0mU6/nZi+YBIfJPSzL4oMPPrjSpyFwiQiCSUBAQOAa5tZbb+3x9YCAALdCpieHiaO35DaOuLg4zJs3r0/rOuLp6elU2rZ8+XLMmjULADBu3DgMHToUAJCbmwuGYfDUU08hLi6Orp+amsoTTRzcAJu7DpFI5JRGOHXqVAQFBeHEiRM0hEMqlSImJobOjWEYBp2dnTzB5O/vj4CAALzyyiu8a/f390dFRQUyMzNhMBho8lhsbCyOHDmCTZs2UYdp7NixOHbsGHWYfv31V9xzzz04f/486uvrsWTJEtTW1qKpqYk6TKdPn8bq1asRFhaGW2+9FSEhIdBoNNiwYQPWr1+P7du3U9EokUgQGxtLPwPZ2dk4dOgQurq6wDAMTzAB5POi1+uxd+9evPvuu7BarXTeU3FxMYYNG4Zp06YBsH02lEolDRfhen6Fh4dDp9Ohs7OTCibAOe1twoQJGD58OG8ul0ajQVNTExobG6nj9sUXX0Cn0/Gi4d9++210dXXhrbfe4rl89l9zApA7NveaQqHAxx9/jGPHjjmFNOh0Ovpz0dDQgOPHj8ORXbt2Qa1WOwkm+9LA8vJy5OTk0LJOR8F04MAB1NbWQi6X07JIV7Asi6ioKJfx9q4oKyuDSqVCd3c3fvnll371puIEU28OU3FxMZ23ZLFY4OPjg46Ojkua93Wt0dTUhLKysn5v19bWhsLCwstwRgJ/BIJgEhAQEPgT05Momjt3LnJycgbkOF5eXggPDx+QfQHE+eHEyaxZs5wi1blUPY6hQ4di2bJlTvvx9/fvdQ7WfffdB29vbxQXF9OkuoyMDOTm5vLunUwm4wkmb29vLFy40GVwhslkotHrDMOgsbERycnJUCgUKCws5Amm4uJihISEIDg4GOvWrcP69etx4cIFqNVqFBUVYc6cOcjLy4NcLgfLsigpKUFBQQF14ORyOY4cOYL4+HhERkbC09MTIpEIkyZNwiOPPIKYmBja9JhhGIwdO5ZGh3OCqbu7Gx4eHo0sX6MAACAASURBVLSfll6vx8SJE1FRUYEpU6Zg9+7dqKurQ0xMDO/6a2pqUFZWRt97lmUhFosxevRo3HfffQDIADM8PBxVVVVYvXo1nffDsizeeOMNjB07FmvXrqWNdLlBt06no/PKKioq0NLSgpSUFOpmHT16FO+99x7q6+upKPD29uaJD3vBpFQqYTabwbIsVqxYAaVSiXPnztGSQw6NRkPdPJ1O5yTy9Ho9nn76adTU1FCh2NnZ6eTElJeXY9q0aWhsbOT1C+MoKChAeXk5fH19nT4/9pjNZqSkpPTZYWpuboZKpUJ+fj5ycnL6NffJ3mFqampyK5i2bt1K3yeLxQKZTAaNRvM/0Y+psrISBQUF/d7OYDBc8cbKApeOIJgEBAQE/sQEBQW5FUw+Pj5uU/KuNbgwBkdEIhGv8W5PiMVi6lpFRUUhMTGROiMAEZ/2bgTDMMjNzXW5r4iICIwcORKxsbFgWRZGoxFhYWFQKBQYOXIk/Pz8MGXKFMyaNQssy4JhGMjlcgwZMgRTp07F/PnzUVVVheeffx5PPPEECgoKEBwcjNTUVHz33XeYOnUqTzCdPn2aVxYIkDlukZGRiIqKwr333kuXDxkyBMXFxQBInyyVSgWDwUDDQbjzyc7ORk1NDUaMGIFz587RqHeOgIAA7N69G1u3bkV4eDhNXZRIJEhOTsb8+fMBEDHh7e2Nu+++G5s2beI9ZefmlS1evBh5eXlYtWoVbR4cFBREB+WVlZVUMDU0NKCmpoYK0JaWFloKJpVKUVFRQUWSTCajX7e2tkKn06G6uhqlpaXQ6XQ4d+4cjfgHyDynkpIS6ubpdDonwZCfn48pU6ZAoVAgICAAnZ2dyMvLw7Zt23jr1dbWYvTo0VAoFE5OrVarhU6nQ0NDg9uIdg6TydSnsj2OkJAQNDc34+eff8ZNN93UL8Gk1+sRHh6Ojo4O/N///R+MRiPPsauvr8exY8dw8eJFKlwtFgsCAgLQ0tLSp4AYALT0dKDoT9lhX2hpaXErbsxmc7/nhgHk3v4RwR0Cl4drSjAxDJPEMMx7DMP0HNkiICAgIACAuEh9FQx/VlwFDrhizJgxvFAHx20DAwNdRka7YsSIEZgwYQIWL15MgxSkUikUCgU2btwIPz8/xMTEICEhgcasx8bGYv369fD09MSSJUsQGxuLsWPHYvr06SgoKIBcLseMGTNQXV2N5cuXIzY2FgARTIWFhbwSRXtEIhHvtfT0dFpS5OHhAYvFwgt34JrcpqWlUWdv+fLlvNAJgIiR8vJynDlzBklJSfj111+RlJQEPz8/OufMnsmTJyMoKIiW1NmLiPDwcBQWFqKlpQU1NTVISkpCYmIiTp48iXfeeQdGoxGNjY1ISkrCnj178OWXX/LKLLleURKJBPv27cOPP/4IwBZRb7Va6fuQn5+PjIwMBAUFobKykieY8vLycOrUKZ7D5FjSeu7cOYwZMwYKhQKpqaloamrCxYsXeSEdAHF6UlNTXYZUrFy5kgome4epo6MDc+fOpSKlvLwcJpPJbdKl4/HMZjPCwsKg0+nQ1taG8PBwmEymPjeUVavViIyMRHt7OxQKBZqamnjHfvPNN/HFF19AqVQ6CSaNRoOAgACXfbIcKS4udlnq+NNPP/WayueKf/7zn/3epie+/fZbFBUVuXzNbDb3GoThCoPBwBNMA93kd9++fTxx29bWhra2NnR1dV3RUsm2tjZUVlbimWeeuWLnMBBcccHEMMz7DMOoGIY567D8RoZhyhiGqWAY5gkAYFm2imXZe67MmQoICAhcewwZMqTPT33/rNi7RD3x4osvOi2zF0xBQUF9FkwvvfQSRCIRxGIx7cvk4+ODxsZGZGVl0cG+p6cnHnjgAQBEQNgPzl955RUEBQUhODgYra2t8PX1RVhYGF5++WVkZ2fTc5HL5SgrK6Nld73h7e0NlUpF53KJxWIolUoqmCorKzFs2DB4eHjg+eefBwAkJibiiSee4O0nICAAdXV1uPnmmxEVFYVjx44hPT0dc+fOxahRo+h6fWmuywmmuXPnYv/+/cjMzERmZiaOHj2K/fv3IykpCZWVlYiMjMTFixdRXV1No9lDQkKoGyCVStHU1IQPP/wQQ4cOBcMwUKlU2Lp1K+Li4qhgmjBhAvR6PaKjo3lz2pRKJYqLi3kOU1hYGLRaLe666y4UFBSgpaUFSUlJUCgUyM7ORklJCerq6qiw8PDwoE2ORSKRywbBZ86cgdFodHKYtm3bhoyMDFRVVaGrqwt33303r2/Xhg0b3DbUff311/HMM89g2rRpVGT5+fnBZDJh48aNOHv2bI+NrLn3yn4+kkKh4N0fsVgMHx8f+Pv703JJi8UCf39/qNVqhISEOJXl2ZdIcqKyqqrKpZDMy8vr9zwfi8XiJFYBkhj5yy+/9GtfHEql0mV8PXBpgslgMNA5fRwbNmxw20ftUti5cycvcfLbb7/FF198gbKyMrzzzjt93s9bb701YOcEkLmDn3zyCX744YcB3e8fzRUXTAA+BMCLeWIYxgPAZgAzAQwGcDvDMIP/+FMTEBAQELjWyc7O7tN6jvOkAODOO++kX6ekpFBXpzfs3ZOoqCiIRCJIpVIYjUan0kEuZt0RbpDMMAxGjBhB9zlp0iTeejKZDMHBwU7uWE8sXrwYw4cPB0CCF77++msqmPz9/TF4MPmTm5aW5nYfnGPzzjvvICgoCEePHkV6ejrv2q1Wq9N5WSwWPPbYYzwhER4ejqKiIsyYMQNHjx7FuHHjMHHiRJw6dQoNDQ1ISUlBVVUVQkJCMHHiRLS0tNBGwLGxsVQ8SiQSKJVKqFQq2u/ryJEj2LVrFxITE5GXl4eMjAzExcWhra2NXidHZGQkfv31V8jlcojFYrS0tGDcuHF47733sGjRInz00UfQ6XSQyWRoaGjA5MmTcfLkSZqs19XVBYlEgk8//ZQ6CGazmZcqWFJSAl9fX/j6+jo5TDU1Nbjvvvvw448/orS0FB0dHVRUMQyDhoYGbNmyBQCcBu0eHh44evQoRo8ejTNnzmDEiBFUMBUUFOD8+fNUnPcE58q1trbyYsK5BsTp6em8uWScw8QJJvv4dKPRiJtvvpluv2nTJgCkbQAn3srLy2mgBcuyvJ8PV1Hsjtet1Wp5wSXfffcdFAoFLl68iK+++goAnITi+vXrnfZr73i5E0wbNmygJXkWi4WWtvbGRx99hNOnT/PcusbGxksq7XNHTU0Nr5dcVVUVzp07B5VK5bKxtDvee+89VFZWXvJ57Nmzhzq+ABGLhYWFOH/+fJ8enlytXHHBxLLsIQCOXuEoABW/OUoWAJ8AmNvXfTIMs4xhmFMMw5yyf9MEBAQEBP73WLFixSVva++UDB06lMaV94fo6GhERkZCKpX22aFy5IYbbnD7moeHh9P8pd4YPXo0Zs6cCYBc1/Hjx6lgWrBgQZ/OUyaTwWKx0Ej5lpYWp/k4arUaiYmJvGWTJk3i9QoCyFwqzrmRyWQYNmwYRo0ahfb2doSFhSE5ORmNjY3w8fHBhg0boNFoEBoais7OTqSnp9O5ZVKpFCaTCW+88Qad4xUVFQWpVIrExEQcPHgQEyZMQFRUFMLDw3liVaVSISEhAWq1GoGBgYiNjUVtbS0mTpyIjRs3YvTo0XjxxRcxd+5cBAQEQKFQIDg4mAZJzJ8/HydPnqSx6twg/oYbbuDdl7NnzyIzM5OKLj8/P7AsSwVJXFwcjXVfsWIFfvjhB/j5+SE6OhoZGRnw8fFBe3s75syZw7uvnp6eOHjwIBiGgYeHB3Jycqhgkslk+P7775Gfn489e/b0WPYmkUjQ0dHh1JusuroaiYmJmDZtGm677TaeYJLJZC4F044dOxAaGoru7m5oNBo614wLGQGANWvWIC8vj27DNXDu6urC0qVL6fLjx4+jtrYWK1eu5J2vVqvlHfPQoUOorKyERqOBSqVCaWkpNm/eTF9nWRafffYZLdsESADH2rVrAZCmyUajkSeYOMFx7NgxmM1mmM1mKJVKfPLJJ7xzsd9Gq9VSB8lkMuH8+fO88k+lUtmrU1VSUtLj6wARohs3boRGo+EJJqPRCA8PD6hUKpfC0x25ubn44osv+ry+I2VlZTzBZDQacerUKWRkZLh17a4FrrhgckM0gDq77+sBRDMME8wwzBYAwxmGedLdxizLvsOybA7LsjncL0wBAQEBAYErQUxMDG688UZIpdJeY9zd8dhjj/X4uv3Asr8EBwejqqqKDubuuadvle9cry6AzO9y1eTYz8/PSWTeeOONmD59Oi9AQyqVYtCgQWAYhtfYOCcnBykpKUhKSqJP42NiYiCXyxEaGoqIiAgkJibSJ/dSqRRmsxljxoyh+16/fj2Sk5ORlJSEoqIiJCQkID4+HklJSVi5ciUYhoHVasXzzz+PJUuWIDMzk877qq2thUwmw+zZsxEUFAQfHx9MnToVMpkMjY2N8PPzo9HuQ4cOxZYtWyCRSDB69Gha2jR16lSaGMj1dkpPT0d7ezv0ej38/PzQ1dWFv//971i8eDEA4P7778fBgwexaNEiHDhwAP7+/vjLX/6CW265BWPGjMGaNWsAkAH6unXrqJPCpUfeddddkMvl8PPzQ0NDAxISEmh8/eHDh6kzwrKsk0vCOUwhISG8OTfHjx/H0KFDERoaitGjR7t0mDgBCQBr165FU1MTZs6cibKyMmg0GqfGtnq9Hunp6bwyw/DwcOqKXLhwgS7/+eefcfToUZw5cwa7du2ipX5arRahoaH4+OOP0dXVhZqaGhpJn5iYiP379/McE71ej1tvvRWHDx+my/bu3Us/p//85z9pvD5AHCtujtTFixdhNpshkUig1+tRW1vLu54777wTO3bsAEDE1X333Qer1QqTyYTS0lJeIqNKpepRMLW2tuLVV191Wq5Wq1FXZxsiFxYW4pNPPoFcLodWq+U9jPDw8EBjYyPCw8PdlmOeP3+e3h+WZSGXy/vcHNoVOp2O55wZDAakpqYiNzcX17KJcbUKJleNP1iWZdUsyy5nWTaZZVnnYnMBAQEBAYGrDLFYjBkzZsDT03NAo9ftmTFjxiVvyzAMQkNDneK1e0Mmk9HrGTp0KO6//36ndcaMGUOj2u2ZMGECRo8ezVuWlZUFANi4cSNdduedd2LevHkYMmQIb35GTEwMQkNDERkZidDQUFpe6KpBbEZGBsaPH4/BgwdDKpXCy8sLUVFRVGRKJBKUlpZiyJAhCAoKom5dfHw8Hfw7zuvw8/OD0WiEt7c3Ro4cSZshJyUlQSqVIjs7m84dZBgGYrGYliICQGhoKIxGI2QyGR1833vvvbQPWHJyMrZv3w6pVErnIkVFRSEoKAjTp0/HiBEj8Pjjj6OwsBBardapee/tt99Oz/PcuXNITEyEr68vFi9ezHNNz549S8VAd3c3RCIRPZ+oqCjqflksFhQWFtL3SCqV0vfDsSTv448/RnV1NQ1NyM7Oxo8//uiySfCFCxeQlZVF98WyLCIiItDU1ISTJ08iPT0dLMuis7MT9fX1OH78OEwmE3bu3EmDDDQaDTIyMvDMM8+goaEBQUFBaGxshMlkwqxZs/D+++/zHL76+nrEx8fDarXi7NmzOHDgAKxWK52rVVBQQF0kgAifESNG0G11Oh2kUil0Oh2vN1Z3dzeGDx9ORZ5arcbixYuxfv16dHZ2oqysjOcwiUQimEwmmlLpyNmzZ10uP3nyJA4dOkS/P3LkCMaOHYvBgwdDq9Viy5Yt9N7Hx8ejqKgIQ4YMcdvHq6ioCBs2bABAAhp8fX3h5eXFm2+1fft2t3PL1qxZg1OnTtHvHQWT0WjEkiVLMGHCBOzevfuSItmvBq5WwVQPwL5QPAZA3zq2CQgICAgIXKX0NbHvjyYtLa3fgikgIIAO8L29vZ1S9ADSWNmxKTFAAjSmTp3KW/bKK68A4PfYWrRoEUaNGoXIyEheIMaiRYsQFRWFG264AcOHD8fDDz8MgAzkXc2TuOWWWzB48GCnOUvcNp999hlmz54NADToIjo6mqblOeLh4eFW/I4fP95pnhlA5upwg8WQkBDIZDJER0eDYRj84x//4Lli9kyaNIk398XDwwMLFy5Ebm4ujh49Ck9PT7dzQ3x9fVFaWoro6GgsXboUc+bMwW233UZdtR9//BHp6ekwmUxobm5GSEgIxGIxmpqaaHkkQJol25cvcts3NTXBYrEgJiYGFy5cQHBwME6fPo3q6mpakpiamorW1lacPn0acrkcer2eCocLFy4gNTWVd/6cYCovL8eoUaOgUqkwZ84cyGQynD59Gunp6Th16hQVTFqtFpmZmQgNDUVVVRWCg4Ppa9nZ2ZDJZLT875dffkFeXh5iYmIQFRWF77//nvYFE4lE6O7uRkxMjMsSNqvVivb2dmg0Gnh7e0On0/FcUoVCwZsHqdFoMGnSJBiNRkilUvj5+fFCQLiG0J9//rnLRL6ioiL6+eMi60tLS1FfX88Li9BqtbjjjjswZcoUaLVa1NTU4MSJE5BIJEhNTUV5eTnCw8NpOaQjRqMRPj4+KCgogFqthlwux4gRI1BYWEiPU1ZW5rKMU6PRQCqVUocTII6SfemdwWDAjTfeiISEBHz66ae8da8lrlbBlA8glWGYRIZhvAEsArD3Cp+TgICAgIDA74JzQq42HnzwQd7gry94e3vj8ccfH7Bz6Ck6OyAgACEhIfT7YcOGwdvbG+Hh4bztJBKJUww4B8MwLksXpVIpamtrqZjlygy9vLx6nBvmTvwmJCS4FJ/cfCzO0QsKCqID7Pj4eKdeTRxr1qzhXTtHYGAgysrKkJ6eDplM5vKYXl5eqK6uRkxMDO/aw8PDoVQq0djYiPvuuw933303qqurERcXB4lEgsbGRoSEhCAuLg6enp44dOiQU8nlkSNH8PLLL8Ni+f/bu/voqqt7z+PvbxKSkJw8E0IIJCEJSXgOjrTcC8gFrTwoD7e1LfXOAPfCpait4rSdBXTasa7VLm/rumi1HcdlL6jrUnV8brmWgeptdaoO4EPrUCigRAhE1JCEIAMCe/44v/PznOT8CAGSk4TPa63fOufs39M+yfZHvu69v/sUo0aN4sCBAwwaNIh3332XQ4cOceTIkZhEJX/4wx/Iz89n3759VFZWAuGMjBUVFcBn85qKior43e9+R2lpKZWVlbz11lt++vsjR44wdepUhg0bRlNTExs2bODFF19kxowZrF69mq1bt/rXg/C8rkcffRQz484772T//v3cfffdDB8+nJtvvplvfetbJCUl+dkW9+zZw/Tp0+Nm1mxubmb48OG0tbVhZjQ3N5OXl+cPdXvvvfdi5utFkoNE1NbW+j2NZ8+eJTs7m2PHjvH222/H9MhEeoIaGhr89vHjH/+Ys2fPsnbtWhoaGmKGzJkZdXV1XHPNNRw9epRQKMQjjzzClClTqKqqoq2tjUGDBsUNmO666y7a2tpYs2YNTz31FE1NTRQUFDBp0iR++9vf8s1vfpOWlhb/v6n2w/o2b97MddddF9N209LSOvQwZWVlMXjwYAYMGMCJEycCg7feLOEBk5n9EngVqDGzg2a2zDl3GvgGsBn4M/CEc67zmW8iIiK9WGToVm8TGXLUW7UPmIIMHDgwMGACuO666+KeEz3cL1qk1yuervYW5ufnM2HCBDIyMiguLmbw4MEMGzas0/OiM+y1d9NNN/nDDYPSyp8+fbrDWmzDhg1j/fr1zJw5k6qqKmbNmsUrr7xCRUUFaWlpHD58mPz8fFauXMnkyZPZtWtXh/l3kXXJTp06RVpaGtOnT6egoMCfQxT9x3VFRQXbtm2jpKSEnTt3+gHTyZMnSUtL84PWgoICvzdkxYoVTJgwgfvuu4/bb7+dNWvWUFRUxPXXX8+iRYv4+OOP2b59O5s2bWLs2LHMmTOHxx57jKuuuiqmnkOHDmXo0KHU1dWxZMkSxo8fH/PziCy4PGTIEN566y3GjRvXYc2gSHr6iooKf15Xc3MzNTU1bNq0CfgsYDIzzpw546eVjxg1apSfhKOpqYnS0lLa2tpoaWmJCTC++tWv+sGmmXHq1ClOnTrFvn372LdvH3v37o37e45krYwsZzB58mSKioqorKz0FzMGuPfeezl48CDbtm3jmWee8VPQp6Sk+PPQiouLefbZZ2ltbWXXrl2MGjWK6urqDtnzPvroo5if5WOPPUZ2dnbM9zl9+jQpKSkUFhYyc+ZM1q5dG3cJh94u4QGTc+5rzrli59wA59ww59wvvPJ/c85Ve/OVfpjoeoqIiEhipKens2TJkvM6rqtDC88VZJ0rWOlqwPT973+f6upqSktLKSkpYfny5SxfvrxL12hv8uTJlJSUMGPGjLjBIITT4bdPZT9s2DC2bNnC7Nmz/WO2bt1KeXk56enpvPHGG4wcOZKJEyfyhS98gfXr13e47saNG0lJSfEDjnvuuYe8vDwKCwv9DIKRnoeioiKOHj1KeXk527dvZ8SIESQnJ/up1+vq6nj88ceprKwkNTWVDRs2kJqaSnFxMQsXLuRzn/schYWFTJo0idraWubOnUtjY6PfC5acnMyAAQMoLi6mvLy8w6KwK1asYP78+TH1jkhLS6O4uNgPmIYMGeIPC430eqWkpNDQ0EBlZWVMwHTrrbeydetWIJzWu7S0lIkTJ/rDOqP94Ac/IBQKcfz4cf7yl78wYcIEWltbycnJiQkwxo8f7w/BA/y1znbs2EFxcTHvv/++/3P95JNP/IyWSUlJfpD68MMPk5aWhpmxevVqCgsL/V6dQ4cO8fOf/5wXXniBmTNnxmQsbGpq8ocBjh49mrq6Onbu3MmoUaMoLCzskLQjelHllpYWfvKTn5CVlUVzczP33ntvzLGpqan88Ic/JDs7m+Li4j6XACLhAZOIiIjIuUTWoupMZz1M8UTmenTVTTfd1KXjq6qquOaaa/wU8WbGhAkTunzfeM6VgbG2trZDWU1NDevWrfP/8K6qqmL37t1kZmaSmprKokWLKC8vP+c909PTKS0t5cCBA5iZv7BzTU0Nhw4doqqqyv/j28yYNGmS3wMVCoXIy8vzk5VcccUVPPDAA/7aWdGWL1/uz3mKZI3Lz8/3k0JE5q8B/OxnP8PMqK6u5oMPPohb7/YLeVdUVFBSUkJ1dTVbtmyJ6TF5/vnnKSoqIhQK8e677zJixAh/keTTp0+Tk5PjD8s7deoUAwYMYMGCBZhZTNIE5xwFBQVkZmbS1tbGyy+/zIwZMzh48CC1tbX+nJ+2tjbKyspiAqj9+/czZ84cP7lDZMjo9773Perr6ykrK/OP3bt3L0VFRZSWlvpln//858nLy/PndaWnp/tDHKN/Fmbm9zBBeD2m4cOH8/LLL1NWVkZubm6HtOAnT570k2W8/vrrZGdnk5SURENDA/fffz8PPfRQzPy0SHu7/vrr+c1vfhP399Nb9duAyczmmdmDfTnnu4iIiJy/UCgUN735uWRnZ8fN5NeZeAkkOhNZZLgn3XbbbR3KMjIy/Ix3EO4ti8y/SUpK4kc/+tF5Xbuuri4mYUF0wFRZWRkzTPArX/kKNTU1/jpKK1eu9AOkoqKi81r/KzLELRQKsW3bNmpqarjxxhv9/ZEA9IYbbuiQhTHI3LlzmTp1KoWFhdx8883+MMmDBw9SX1/PrFmzyMrK8udbHT9+nIKCAj8hQm5uLo2NjTHBR3V1tZ8qPTMz0x/yGcmu2NraSnZ2NvX19dTW1nLixAl/vahIBr9IcFFfX09FRQVNTU2MHz+eFStW8PHHH/Poo4+yZ8+emIBp/vz5cefpJScnc/bsWb83cMyYMX5mwEh6dsBP+hD5WVdVVVFYWEhycjI5OTn+3KnnnnvO780yMzIzM3nppZeYNWsWJ0+epL6+nlWrVvHGG2/Q0NDQoT4jR468qMVxE6HfBkzOuV8551Z0tWteRERE+qa0tDTmzp3bpXOmTp3qD03rj85nUeOkpCS+/e1vd/naY8aMiUlXnZeXx5w5c/jwww+ZPn16TNCybNkyxo4d6wdJ7YcJxltzKIiZ8dFHH/lZGtvLyMhg6dKl53WtgoICf1jZsmXL/BTkd9xxB6tWraK8vJxQKMT+/fspKytjwIABTJkyxU+lXVhYyObNm7nyyiv9a44dO9Yf1peXl+f3SlVWVrJz505/34EDB/yevE2bNrFx40ZKS0v9taXMjAMHDlBSUsK0adOorKxk1qxZpKWlccstt/Dggw8yZswY/76LFi2KyTLZ3vvvv09ZWRmzZ89m8eLF5ObmxiSQaN/rNG3aND/tfHQP07p162hqavJ7j4qKimhoaGD06NGEQiEaGxu5+uqrWbZsWcwaWxGR4Zjr1q3r9PfTW/TbgElERESkM5HJ9Ze7rgaaEO5Ril6YOCMjg9mzZ3Ps2DFGjhzZ6bC+izFu3DgyMzO77frRCTlCoZA/32jgwIGMHz/e74kpLCzk1VdfjQneqqur/aGQubm5fmr9kSNH8vTTT/tBbGpqqj987ujRo1x11VWUlZUxePBghg4dSkZGBq2traSkpLBy5Ur/vFWrVrF8+XKWLFly3nP2zp49y6ZNmxg3bhy5ubnU1tZ2CJjaGzhwoP89I8eeOHGC5uZmGhsb/eOKioqYMmWKn4o+IyOD0tJSxo4dG5iav7W1NSYpRm+X0vkhIiIiIiIdffnLX+5QlpSUFLNYbHdYsWJFt14/WiS7XSgUYuDAgaSkpPiJHQoLC9mxY0fMvKHU1FTWrl0LhHuYIj+LpKQkPv30U3/NrYqKCj/oS0pK4u677yYtLY2qqipKSkpobGz0e6eig/pIMBfvZx9k4sSJZGVlxSxtkJOT4yexAALX84JwMHz8+HGWL1/OihUrYgKmq6++GuccycnJlJeX89prr/lBYlDPZXV19QUF6YmigElERERELsjXv/71DmVZWVndHjB96Utf6tbrR4uk3c7MIqJyaQAADQlJREFUzPTnWUWG4BUWFnLs2LHAoXC5ubkxa4XdddddfobFhQsXAnD48GHq6ur84XCLFi0iKSmJ3bt3xwQ0F+OLX/xi3LpFApvMzMzARBkQDtiOHj3KpEmTuPbaa3nttdf8fdHDK0OhECtXrvQ/By3IfMstt3T5OyRS3+kLExEREZFeb9CgQX72tP4gFAoxePBgUlJSOmQjHDRoUEzihfby8/P9LH9ATLKNxYsXA+FU3zU1NX55cnKyn0yhqwtKd0Vubq4f2GZlZcVk54unvr6e8vJyhgwZEtPD1N60adMuaT17AwVMIiIiInLJdHWNqt4uFApRVFQE4GcTjEhPT/dTxcczYsQIli1bds7rZ2VlxQRM0fftzp9lTk6O3/uVlZV1znXHABobGykrK/OHKF5OFDCJiIiIyCVTUlKS6CpcUtnZ2VRWVgL4c5Oifec73znn+Z0FIuPGjYsbGGVmZlJcXNyFmnZN+x6moAQN0fXpzkQevZnmMImIiIjIJXPHHXckugqXVCgU4tZbbwXoloyKa9asiVteXl7e6dpUFyMzM9NfFys7O7vTNcJKS0v9haHPnDnTp7LcXax+GzCZ2TxgXlVVVaKrIiIiInLZ6M5035eTkpKSbu2tMzMGDRoEhHuYOguY1qxZ4weMoVCIkydPdlvdept+Gxpq4VoRERERkc6NHTuWefPmnfOY6HlWU6ZM6fZMiL1Jv+1hEhERERGRzqWlpflpzc/H5MmT/Xldl4N+28MkIiIiIiKXXkpKSrcmpOhtFDCJiIiIiIgEUMAkIiIiIiISQAGTiIiIiIhIAAVMIiIiIiIiARQwiYiIiIiIBOi3AZOZzTOzB1taWhJdFRERERER6aP6bcCkhWtFRERERORi9duASURERERE5GIpYBIREREREQmggElERERERCSAAiYREREREZEACphEREREREQCKGASEREREREJoIBJREREREQkgAImERERERGRAAqYREREREREAihgEhERERERCdBvAyYzm2dmD7a0tCS6KiIiIiIi0kf124DJOfcr59yKnJycRFdFRERERET6qH4bMImIiIiIiFwsBUwiIiIiIiIBFDCJiIiIiIgEUMAkIiIiIiISQAGTiIiIiIhIAAVMIiIiIiIiARQwiYiIiIiIBFDAJCIiIiIiEkABk4iIiIiISAAFTCIiIiIiIgEUMImIiIiIiARISXQFuouZzQPmAa1mtifR9fEMAj5KdCWkT1LbkQuhdiMXQu1GLoTajVyI3tRuyoJ2mHOuJytyWTOz7c65KxNdD+l71HbkQqjdyIVQu5ELoXYjF6KvtBsNyRMREREREQmggElERERERCSAAqae9WCiKyB9ltqOXAi1G7kQajdyIdRu5EL0iXajOUwiIiIiIiIB1MMkIiIiIiISQAGTiIiIiIhIAAVMPcTMZpvZbjPba2arE10f6XlmNtzMXjKzP5vZ/zWz27zyfDPbYmZ7vNc8r9zM7Kdem/mjmV0Rda0l3vF7zGxJVPl/MLM/eef81Mys57+pdAczSzazN83s197nEWb2utcGHjezVK88zfu819tfHnWNNV75bjObFVWu51M/ZGa5Zvakme3ynjt/peeNdMbMbvf+jXrHzH5pZul63kg8ZvYvZnbEzN6JKuv2Z0zQPbqVc05bN29AMrAPqABSgbeB0Ymul7YebwfFwBXe+yzgL8Bo4MfAaq98NfBP3vu5wAuAAZOB173yfOBd7zXPe5/n7fs/wF9557wAzEn099Z2ydrPfwY2Ar/2Pj8BLPLePwDc5L2/GXjAe78IeNx7P9p79qQBI7xnUrKeT/13Ax4GlnvvU4FcPW+0ddJmSoD3gIHe5yeApXreaAtoL1cBVwDvRJV1+zMm6B7duamHqWd8DtjrnHvXOXcKeAxYkOA6SQ9zzh12zr3hvT8G/JnwP04LCP9hg/e60Hu/AHjEhb0G5JpZMTAL2OKca3LOHQW2ALO9fdnOuVdd+CnySNS1pA8zs2HAdcBD3mcDZgJPeoe0bzeR9vQkcLV3/ALgMefcSefce8Bews8mPZ/6ITPLJvzHzC8AnHOnnHPN6HkjnUsBBppZCpABHEbPG4nDOfd7oKldcU88Y4Lu0W0UMPWMEuBA1OeDXplcprxhCxOB14Ei59xhCAdVwGDvsKB2c67yg3HKpe+7B/gvwFnvcwHQ7Jw77X2O/l377cPb3+Id39X2JH1bBfAhsN4byvmQmWWi542cg3OuAbgbeJ9woNQC7EDPGzl/PfGMCbpHt1HA1DPijetWPvfLlJmFgKeAVc651nMdGqfMXUC59GFmdj1wxDm3I7o4zqGuk31qN5eXFMJDZf67c24icJzw0JUgajeCNxdkAeFhdEOBTGBOnEP1vJGu6tNtRQFTzzgIDI/6PAw4lKC6SAKZ2QDCwdK/Ouee9oo/8Lqe8V6PeOVB7eZc5cPilEvfNgWYb2b7CQ9fmUm4xynXGzIDsb9rv314+3MID5noanuSvu0gcNA597r3+UnCAZSeN3Iu1wDvOec+dM59CjwN/DV63sj564lnTNA9uo0Cpp6xDRjpZZlJJTwx8vkE10l6mDeu+xfAn51z/xy163kgkhVmCfBcVPliL7PMZKDF63reDFxrZnne/w28Ftjs7TtmZpO9ey2Oupb0Uc65Nc65Yc65csLPjhedc38HvATc4B3Wvt1E2tMN3vHOK1/kZbUaAYwkPKFWz6d+yDnXCBwwsxqv6GpgJ3reyLm9D0w2swzv9xppN3reyPnqiWdM0D26T3dnldDmZw2ZSzgr2j7gu4muj7aEtIGphLuT/wi85W1zCY/3/i2wx3vN94434Gdem/kTcGXUtf6B8CTavcDfR5VfCbzjnXM/YIn+3touaRv6Gz7LkldB+A+QvcD/BNK88nTv815vf0XU+d/12sZuojKa6fnUPzegDtjuPXOeJZyBSs8bbZ21mx8Au7zf7aOEM93peaMtXlv5JeG5bp8S7hFa1hPPmKB7dOcWubGIiIiIiIi0oyF5IiIiIiIiARQwiYiIiIiIBFDAJCIiIiIiEkABk4iIiIiISAAFTCIiIiIiIgEUMImIiIiIiARQwCQiIr2amSWb2T+a2e/MrMnMPjWzI2b2RzN7yMzmRx271MycmS1NYJVFRKQfSUl0BURERIKYWTLwa2A20AxsIrxAYj5QCdwI1BJe+V1EROSSU8AkIiK92dcIB0tvA9Odcy3RO80sA/h8IiomIiKXBw3JExGR3uyvvdcN7YMlAOfcJ865lwDM7N+B9d6u9d7QvMhWHjnHzFLM7GYze83MWs3sEzN708y+YWYx/y6aWbl3/gYzqzWzZ71hgcfN7BUzu7Y7vrSIiPQe6mESEZHe7GPvtfo8jt1AeNjeAuA54K2ofc0AZjYA+BUwC9gNbAT+HzADuI9wb9V/inPtEcCrwDvA/wCKga8CL5jZjc65x7vypUREpO8w51yi6yAiIhKXmU0EXif8P/j+FXgG2OGcqw84finhXqa/d85tiLP/DuC/AfcDq5xzZ7zyZOBB4B+Ahc6557zycuA97/S7nXPfibrWlYSDqDagzDnXelFfVkREeiUNyRMRkV7LOfcm8B+BD7zXp4D9ZvaxmT1jZvPO91recLtvAI3A7ZFgybvPGeBbgAP+Ls7pLcCd7eq2nXAQlwv8bVe+l4iI9B0akiciIr2ac+4JM3uG8LC5qcBE73UhsNDMHgGWus6HTFQDBcAe4L+aWbxjTgCj4pS/4Zw7Fqf834ElXp0e7vzbiIhIX6OASUREej3n3KfA//K2yBC6LwH/AiwmPFTv2U4uU+C9jiQ8LC9IKE7ZBwHHNnqvOZ3cW0RE+igNyRMRkT7HOXfGOfcEsM4rmnkep0Wy7D3jnLNzbCPinFsUcM0h7a4tIiL9jAImERHpyyLD5CLj6yLzkpLjHLuLcLa8yV62vK64wsyy4pT/jff6ZhevJyIifYQCJhER6bXM7Gtm9oX26yN5+4YA/+h9/L33GklDXtr+eOfcacKpw4uBn5rZwDjXLDaz0XGqkgN8v92xVxJOENFCeEigiIj0Q0orLiIivZaZ3QPcRniu0Ct8luJ7BHAdMJDwmkt/65xzZpYHHAROA4/w2dyj+5xzLV7P0pPAfKABeNF7HUx4btMU4LvOubu8+5d79/w9MB74E/C/+WwdplRA6zCJiPRjCphERKTXMrPhhIOba4DRhAOVdMI9SW8SXnh2o3PubNQ5swkndRgHZHrFI5xz+739RjhF+VLC2e1CwIeEA6N/Ax51zh3wji33yh8G/gm4C7gKSPPuf6dzbnM3fHUREeklFDCJiIgEiA6YnHNLE1oZERFJCM1hEhERERERCaCASUREREREJIACJhERERERkQCawyQiIiIiIhJAPUwiIiIiIiIBFDCJiIiIiIgEUMAkIiIiIiISQAGTiIiIiIhIAAVMIiIiIiIiAf4/Bgs9r+sEykQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1008x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tmp = np.load(\"training_loss.npz\") # the output array also stores the training and validation loss\n",
    "training_loss = tmp[\"training_loss\"]\n",
    "validation_loss = tmp[\"validation_loss\"]\n",
    "\n",
    "plt.figure(figsize=(14, 4))\n",
    "plt.plot(np.arange(training_loss.size)*100, training_loss, 'k', lw=0.5, label='Training set')\n",
    "plt.plot(np.arange(training_loss.size)*100, validation_loss, 'r', lw=0.5, label='Validation set')\n",
    "plt.legend(loc = 'best', frameon=False, fontsize=18)\n",
    "plt.yscale('log')\n",
    "plt.ylim([5, 100])\n",
    "plt.xlabel(\"Step\", size=20)\n",
    "plt.ylabel(\"Loss\", size=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Practical notes:**\n",
    "\n",
    "Fitting spectra with The Payne is pretty fast. Fitting a spectrum typically takes about 0.1 CPU seconds.\n",
    "\n",
    "Training neural networks should not be too slow, either. Training the default multilayer perceptron network with 10,0000 training spectra should take at most take a few hours (with a V100 GPU). We also included the option to train a large ResNet that could provide better interpolation if needed. Training a ResNet should take about a GPU day."
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  },
  "toc-showcode": true,
  "toc-showmarkdowntxt": true
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
